{"as_of":"2026-08-11T11:28:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:27bc956b277f0fbbece342889786ab4ce9a2f7e12d2a3120a6f2e3dd2a1e79ca","coverage":[{"denominator":73,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":73,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T11:24:18.492893Z","state":"measured"},{"denominator":73,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":73,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2506.02585/citation-record","integrity":"/paper/2506.02585/integrity","json":"/paper/2506.02585/citation-record.json","paper":"/paper/2506.02585"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:27.407144Z","title":"Deep learning for single image super-resolution: A brief review,","venue":null,"work_id":"bb49493e-901e-4852-b6f8-2038e5321125","year":2019},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:11.668452Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:4b8b8ac8fa0cef773d1423454d474a7dc17fdfd2d3dde6985a44f9d3dcd8a47c","observation_id":"e9db2558-957d-4dd3-bac8-11bfbba535a4","resolution":{"observed_at":"2026-08-07T11:24:27.484483Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1211.1768","last_updated":"2019-03-04T11:25:07Z","snapshot_observed_at":"2026-07-06T02:59:24.707097Z","submitted_at":"2012-11-08T06:50:44Z","title":"Nearest Neighbor Value Interpolation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1211.1768","snapshot_observed_at":"2026-08-07T11:24:11.758059Z","title":"Nearest neighbor value interpolation,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:11.758059Z"},"links":{"cited_paper":"/paper/1211.1768","citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:cac6b18a4ba165767befb6e7a084a778df42ee80a9deb5a9c483267fcae97d3b","observation_id":"f058cf2e-2ba9-4421-8333-6cc0e78b5fef","resolution":{"observed_at":"2026-08-07T11:24:11.758059Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:27.219074Z","title":"New edge-directed interpolation,","venue":null,"work_id":"e4522d89-b599-41c7-92a2-b24a60f7081b","year":2001},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:11.855647Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:42cf9c11ce9f50dd5accecccb4c9f10103c1147e9c87493715a6b8507343988b","observation_id":"2cf5ef19-4fe9-4a0e-8d6a-7aab12f22e1a","resolution":{"observed_at":"2026-08-07T11:24:27.309320Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:27.051357Z","title":"Cubic convolution interpolation for digital image processing,","venue":null,"work_id":"a700017c-ed85-4de2-832a-f0ba17996a69","year":1981},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:11.928571Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:994ad115b1fbb410e0e9dc196ece9853a6555dee0488d9e2450ce49ab5bc8d24","observation_id":"a4c70cc6-d0e1-40a3-9509-9026e291aa62","resolution":{"observed_at":"2026-08-07T11:24:27.137385Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:26.842341Z","title":"Super-resolution image reconstruction: a technical overview,","venue":null,"work_id":"f7b3a91c-5fb1-4256-88c0-d2a6e8d6986b","year":2003},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.007884Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:901e270cf066f587931e073bab9d0f4fe5c354ffd7cae8060ff84b01ee9d44d4","observation_id":"2b71f6c4-07cc-4c58-9702-1df2f88c067f","resolution":{"observed_at":"2026-08-07T11:24:26.967962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:26.684520Z","title":"Minimax concave penalty regression for superresolution image reconstruction,","venue":null,"work_id":"54134b49-1058-4071-9ddd-a1e12ca452be","year":2023},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.105572Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:00b4a32a56570e47246dfbd33c9ddfb56f9db925892c4c0cd42524bdd71408cd","observation_id":"8f88e92a-cf74-47b5-8c1b-9bddcc2a6f9a","resolution":{"observed_at":"2026-08-07T11:24:26.750524Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:26.505069Z","title":"Image super-resolution via sparse representation,","venue":null,"work_id":"3cbfc690-344d-4930-b38c-d6ecedbe66e2","year":2010},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.229765Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:019fc228481f083d55b51762dd708c51c5859db82b7baba3d4b2707e4ceb44ac","observation_id":"de023609-06b2-4cc4-81f4-73341b8e1367","resolution":{"observed_at":"2026-08-07T11:24:26.583082Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:26.312190Z","title":"Image super-resolution as sparse representation of raw image patches,","venue":null,"work_id":"76ab4385-691c-4b6a-8193-b0602477aed8","year":2008},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.303795Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:362a5393ea4028905edd9f70a7adaa7425f5f1c0aea679f8ed71e2009d426997","observation_id":"ed44ee9e-99e5-487f-96e9-c98a47674b87","resolution":{"observed_at":"2026-08-07T11:24:26.397650Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:26.117040Z","title":"A non-local approach for image super-resolution using intermodality priors,","venue":null,"work_id":"ff8cc29d-cdbe-4c14-9592-a5baef22883c","year":2010},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.380198Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:fcdb249a82f3103b70e96276086f1dde355d1d0199d8c44d2029ef13250197a1","observation_id":"2d2640d3-c1c8-4abb-b513-8b80cdfe35d8","resolution":{"observed_at":"2026-08-07T11:24:26.214897Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:12.474115Z","title":"Learning a deep convolutional network for image super-resolution,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.474115Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:853400bebd2525c84eb04f654c348162acef8e069b4d233c7df2d550715324c8","observation_id":"3048ec99-fd61-4035-bf22-7286df95430d","resolution":{"observed_at":"2026-08-07T11:24:12.474115Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:25.975844Z","title":"Coarse-to- fine cnn for image super-resolution,","venue":null,"work_id":"df4f3fd2-ca08-479f-9d3c-0d9d60a9f033","year":2020},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.572731Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:5302e1d274a0aa709ad8c6db80772a2e0540dbec2516761d1f342400e9aeb533","observation_id":"0a51b970-ca58-4ba0-9f3d-75300ac2c2d3","resolution":{"observed_at":"2026-08-07T11:24:26.005145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:12.642325Z","title":"Image super-resolution using deep convolutional networks,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.642325Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:876ef847a554e61958e365fa601155e58e14ad6d3acb5a5b8fc004e0b7d3838b","observation_id":"4e4e92e9-8e00-4c65-a2d1-fbebe1f64f51","resolution":{"observed_at":"2026-08-07T11:24:12.642325Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:25.782937Z","title":"Runet: A robust unet architecture for image super-resolution,","venue":null,"work_id":"e8af000e-8b07-4f70-951c-0e77af8c4d84","year":2019},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.698265Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:1de402ab66d8a316eba0c693cabf933a4a685515f9fadc51fa85c13e1850c113","observation_id":"b5aa3752-66c9-492a-99df-4f9a52b5301d","resolution":{"observed_at":"2026-08-07T11:24:25.886073Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:25.548846Z","title":"Accurate image super-resolution using very deep convolutional networks,","venue":null,"work_id":"54b98087-fea4-4999-a6aa-76b4cbfaea65","year":2016},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.792928Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:5f3e3402f8a99985024a799fe6bf61c1d70a51bb8bd853321d0cd797c0993b1a","observation_id":"f5419237-a86c-4970-8889-e2a9c50f144b","resolution":{"observed_at":"2026-08-07T11:24:25.671925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:25.301872Z","title":"Deeply-recursive convolutional net- work for image super-resolution,","venue":null,"work_id":"eed51e1a-4f07-47fe-8a53-f001f777deb4","year":2016},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.880057Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:9b6e3f3544882c3ef7ccd3fbfa31351790ebd5128e5e4b0df6c9fa2e0ea22515","observation_id":"cd6dd837-8bcf-4360-b801-1f72dd869aba","resolution":{"observed_at":"2026-08-07T11:24:25.444638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:12.979205Z","title":"Accelerating the super-resolution convolutional neural network,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:12.979205Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:b3d97a801564ba87b49ce7a4a5bc32bd0c04b391dfb92c0a6f6db19af7d82018","observation_id":"457ef080-7626-4387-ad82-43f5f4984a51","resolution":{"observed_at":"2026-08-07T11:24:12.979205Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:25.147066Z","title":"Asymmetric cnn for image superresolution,","venue":null,"work_id":"7ef85dae-ac9c-4fa1-98c0-524c920e8106","year":2021},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:13.071418Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:0ed1fe916b8c1a8cd97edfeb7bf047c8624a135ba1eaac4c1dfbf5cdc8725f42","observation_id":"794baf2e-2db5-4fa0-926c-deb719678af1","resolution":{"observed_at":"2026-08-07T11:24:25.211059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:25.004993Z","title":"Hybrid attention feature refinement network for lightweight image super-resolution in metaverse immersive display,","venue":null,"work_id":"ba88e2d0-919e-4618-8bcf-825d3aaf89e6","year":2023},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:13.185837Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:68ee881863f7dea40ced7d3147474259449ecfcbab241672af4f6664bb8498d9","observation_id":"eeac2432-4cd6-4156-9391-077c6e9d3a2d","resolution":{"observed_at":"2026-08-07T11:24:25.088829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:24.851021Z","title":"Compressed multi- scale feature fusion network for single image super-resolution,","venue":null,"work_id":"4e56ba61-972e-4e75-8c95-da1da9640469","year":2018},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:13.276552Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:a72431d9c9d15cf54982975c6fae28c46c89fe1108c83d27d077150c20d29759","observation_id":"f9160052-52b4-461f-a0c4-e9b854649c6c","resolution":{"observed_at":"2026-08-07T11:24:24.903938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:24.726086Z","title":"Memnet: A persistent memory network for image restoration,","venue":null,"work_id":"c7144ead-e52f-46ea-a5b6-aaaa3f9078cc","year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:13.388117Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:22505ac29479098c36fe6ea7f43f2985a5f1128593aabeb7ba08b1fa49236d9d","observation_id":"435d6f26-bd29-47ae-bede-94d22f3931f3","resolution":{"observed_at":"2026-08-07T11:24:24.783075Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:13.481550Z","title":"Image super-resolution via deep recursive residual network,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:13.481550Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:c9545964cf27ef36e5d1482aa6f3714b26e3fd0776b5a8059873d3cea54b3687","observation_id":"7afa5c46-3918-4989-a5dc-eddac99ecb57","resolution":{"observed_at":"2026-08-07T11:24:13.481550Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:13.565695Z","title":"Deep learning for image super- resolution: A survey,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:13.565695Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:8f902bca238bbf4edb6a2498542ad557920fe7173f85e84c92ef9fa5d25fb224","observation_id":"4ad0dc49-f685-4110-8e8c-f3c8e3a353cb","resolution":{"observed_at":"2026-08-07T11:24:13.565695Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:24.549830Z","title":"Image super-resolution using dense skip connections,","venue":null,"work_id":"7a19be17-6e6b-4506-905a-ef9015ce29f0","year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:13.656890Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:4d7272a3c87fb04dc9c94c691fde5f7a3b207f3c5d38247b01999d7e50213dc9","observation_id":"833e6428-fb34-4f8a-a8ab-84a01261af53","resolution":{"observed_at":"2026-08-07T11:24:24.607825Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:24.414056Z","title":"Lightweight image super- resolution with information multi-distillation network,","venue":null,"work_id":"21aba2c6-d95d-4cd3-94e9-d3059c5fac10","year":2019},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:13.750532Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:ce7015e281545faf075c3de908056b95bce1a2fefbeaedc576187cc273cd0827","observation_id":"60f9c7ce-6e2a-46ea-88e2-5cc2ee5b99f4","resolution":{"observed_at":"2026-08-07T11:24:24.489272Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:13.859108Z","title":"Photo-realistic single image super-resolution using a generative adversarial network,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:13.859108Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:9347c58d36b27b95fd55270031b7837d3c5bc520a00966a92405da88ac211857","observation_id":"1180e194-d686-4e27-adfc-53b1c99f3d41","resolution":{"observed_at":"2026-08-07T11:24:13.859108Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:13.960822Z","title":"Ctcnet: A cnn- transformer cooperation network for face image super-resolution,","venue":null,"work_id":null,"year":1978},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:13.960822Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:8548345c5f99523cceee019dfb3287664da1d31bd27c78e58dc625d151814e27","observation_id":"ac4dc7ce-9246-4047-985f-5df696b4ba56","resolution":{"observed_at":"2026-08-07T11:24:13.960822Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:24.247641Z","title":"Super-fan: Integrated facial landmark localization and super-resolution of real-world low resolution faces in arbitrary poses with gans,","venue":null,"work_id":"15b85e6d-831d-44ac-b498-97938240c09a","year":2018},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:14.030022Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:80e65aacd23ac1013f011015fe94c6e41e941da9ca3836198b595a724d984821","observation_id":"679610eb-4294-4b43-abe2-60b4784e16ee","resolution":{"observed_at":"2026-08-07T11:24:24.292266Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:24.055881Z","title":"Deep laplacian pyramid networks for fast and accurate super-resolution,","venue":null,"work_id":"fb314a86-880a-4cb1-9007-15edba590c83","year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:14.125429Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:5b2c8d3f1859a1c2f3625d5b6407b2210bb1f3f7e1bab28ee82202e35af64c41","observation_id":"d610268e-230e-4c77-ad6f-08745080868b","resolution":{"observed_at":"2026-08-07T11:24:24.160499Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:14.257161Z","title":"Perceptual losses for real-time style transfer and super-resolution,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:14.257161Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:f2c72a281e4ec89c7d10182817a1b28965fda03763db02fd546b7faaa237c518","observation_id":"0bf9b14f-d85f-4fb0-8efe-1d9a4acb2d6a","resolution":{"observed_at":"2026-08-07T11:24:14.257161Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:14.364533Z","title":"A stochastic approximation method,","venue":null,"work_id":null,"year":1951},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:14.364533Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:dc782a8ff91250baf69ec7e5cc1b4c509c026830ac239d7d6f48b0c16b96e905","observation_id":"fda559f8-2444-4e7d-b222-e5efb86b14c5","resolution":{"observed_at":"2026-08-07T11:24:14.364533Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-07T11:24:14.462547Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:14.462547Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:7012e16e83dc9bd936cc6603df3b53a4e4a9bbcb70720a0026c61809dedd2656","observation_id":"896a1428-b5fb-4677-ab1b-ec2177e21246","resolution":{"observed_at":"2026-08-07T11:24:14.462547Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:14.576631Z","title":"Enhanced deep residual networks for single image super-resolution,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:14.576631Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:541a16317b9c57583ecadcae4743960dfcb8b0851b4f6a6c45f6eb8b56d1943f","observation_id":"cdffc9e0-6d02-4ead-8ce2-69df3c4f08a5","resolution":{"observed_at":"2026-08-07T11:24:14.576631Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05101","last_updated":"2019-01-04T21:01:49Z","snapshot_observed_at":"2026-08-09T20:34:52.923500Z","submitted_at":"2017-11-14T14:24:06Z","title":"Decoupled Weight Decay Regularization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05101","snapshot_observed_at":"2026-08-07T11:24:14.676318Z","title":"Decoupled weight decay regularization,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:14.676318Z"},"links":{"cited_paper":"/paper/1711.05101","citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:60f3155384a4e0530c25d1c5ba3b7280e8bfc1c9014a85e154201afe5b045314","observation_id":"5f8ede0f-e492-4960-bc00-b589a5c620e3","resolution":{"observed_at":"2026-08-07T11:24:14.676318Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:14.739838Z","title":"Adan: Adaptive nesterov momentum algorithm for faster optimizing deep models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:14.739838Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:9fe2f64d73cb1c37051d2f399a316079a135bc0e9798382d7a1ee2d8a3a40873","observation_id":"c3d99054-0f8a-47f9-940a-5da24c1ffbd5","resolution":{"observed_at":"2026-08-07T11:24:14.739838Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:23.847102Z","title":"Image super-resolution with an enhanced group convolutional neural network,","venue":null,"work_id":"ac0e58f9-740d-4684-b715-5a9b36197e9f","year":2022},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:14.851921Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:70d4039ca667cb88de8056423d470a3b79856701488aa8c6f840282e0ccfd826","observation_id":"92f44db3-8833-4160-9f7b-a112a13b91b9","resolution":{"observed_at":"2026-08-07T11:24:23.924827Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:23.694136Z","title":"Sharpened cosine distance as an alternative for convolutions,","venue":null,"work_id":"cf254a23-f6a5-4c9d-91c7-da076058f450","year":2022},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:14.961184Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:fdc0e10bb01c85ff8d5b271184ee2140024f9796d81e57cbb2e6046c9c75cdbd","observation_id":"151f5371-3736-4e6e-a240-ab0aeee486d1","resolution":{"observed_at":"2026-08-07T11:24:23.777044Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:23.509845Z","title":"Ntire 2017 challenge on single image super-resolution: Dataset and study,","venue":null,"work_id":"4b77a934-1809-4027-b13c-7359b3e68caa","year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:15.055992Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:23ce8b703c79a32840769d8d6385e7b95574f1f26dd2d3b212b41e80dbec49d5","observation_id":"594c56e0-aadf-4bcf-8136-cb0526952d2c","resolution":{"observed_at":"2026-08-07T11:24:23.571013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:23.307167Z","title":"Low- complexity single-image super-resolution based on nonnegative neighbor embedding","venue":null,"work_id":"f1faac7f-ed91-4871-a866-bc1646035ae0","year":2012},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:15.153099Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:7f45e17b768a01f8c2c0570dc1aaa95f1cb6f8e4d57a6120cfab5125aa16acba","observation_id":"d6def933-dc4a-40d0-af27-a39c1809e726","resolution":{"observed_at":"2026-08-07T11:24:23.406340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:23.107414Z","title":"On single image scale-up using sparse-representations,","venue":null,"work_id":"7e4b3b85-b257-412b-9a33-b60d217cee06","year":2010},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:15.220705Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:cf142487554c065d127d2c6faf8b367f16d112670c220a118ab89a841c4df45f","observation_id":"f402d23b-f07a-4bb5-a71a-5600075aaacf","resolution":{"observed_at":"2026-08-07T11:24:23.202868Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:22.844477Z","title":"A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics,","venue":null,"work_id":"f56c8692-8108-48e2-996f-21b4a75836ff","year":2001},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:15.356921Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:2f96c243375fb69e7651fad24c39f8c5a72e8c549b00cbef2caf282aefb57527","observation_id":"0e3d2f09-413d-4d7c-9c64-7477c0df43fa","resolution":{"observed_at":"2026-08-07T11:24:22.977367Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:22.588849Z","title":"Single image super-resolution from transformed self-exemplars,","venue":null,"work_id":"492a8f59-40e9-427a-8197-7e25d1381d27","year":2015},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:15.461841Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:ace6aee07729c1fe4fb902c6c76afe7bd145aa3c3e9dbf8d25715f479208c9f7","observation_id":"3896e382-da30-46d2-984b-e97e8dfd191f","resolution":{"observed_at":"2026-08-07T11:24:22.734922Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:22.340552Z","title":"Multi-scale attention network for single image super-resolution,","venue":null,"work_id":"72c7e7fc-c742-4005-b078-e1fd4296091a","year":2024},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:15.509301Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:0881c98130b560f8104c7c8be081fcc0fd99851ebed7a44bacdbad2958b3e8e1","observation_id":"7ec6df98-bddf-46fb-ba3b-301c6777374b","resolution":{"observed_at":"2026-08-07T11:24:22.447413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:15.613256Z","title":"Residual dense net- work for image super-resolution,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:15.613256Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:df2d8c1a645f67849fa2b225ca328165bc768729e8a47aa4c561812354dc4b0e","observation_id":"22b695c8-667b-4cc7-a870-ed6b3b040f6c","resolution":{"observed_at":"2026-08-07T11:24:15.613256Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:22.229644Z","title":"Separable and reversible data hiding in encrypted images using parametric binary tree labeling,","venue":null,"work_id":"90072a05-80d7-47eb-ac35-8e6f207f388b","year":2018},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:15.714833Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:12524814f7e4d313d99987da9653e03f5fd614aa8b2c2d55184303a0b4bd12a5","observation_id":"94ceb0a5-31b0-4549-a96b-9342dc959f77","resolution":{"observed_at":"2026-08-07T11:24:22.255641Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:22.104751Z","title":"A heterogeneous group cnn for image super-resolution,","venue":null,"work_id":"f72ec984-54f7-4ce4-930a-c92fb9802443","year":2022},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:15.819083Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:783801684f2a71c8f9dedd65155187f467ab837dc52dd1df98d9b2cf211f2cf6","observation_id":"3ea6f4a2-d93b-4830-9060-28373e343d6b","resolution":{"observed_at":"2026-08-07T11:24:22.171360Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:15.922259Z","title":"Fast, accurate, and lightweight super-resolution with cascading residual network,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:15.922259Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:cebeaae13975286b0caa8840684a2358f4b474ac7825f0169e44e61b43b66685","observation_id":"65bc5a47-d9f2-4295-b9fd-0e806ebaad3b","resolution":{"observed_at":"2026-08-07T11:24:15.922259Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:21.965809Z","title":"Fast and accurate single image super- resolution via information distillation network,","venue":null,"work_id":"2461c1d0-177b-4bcf-ba0b-91e0fa7c4f32","year":2018},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:16.061669Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:5b58ead7d347dabc1c6227254d180aefadc06af8c98a8ae42239533cfd1e0c0b","observation_id":"735ca547-4ca4-4a26-b0f4-5d4ba54bfc12","resolution":{"observed_at":"2026-08-07T11:24:22.003660Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:21.838597Z","title":"A+: Adjusted anchored neighborhood regression for fast super-resolution,","venue":null,"work_id":"aff4c8ce-95f4-4487-99df-e1d464998c75","year":2014},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:16.178970Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:e3adad6af8a326151d5f7712ed67be452727cfc1ba1f53c4283e16a02e6e9b07","observation_id":"1e3de02d-7e28-48a1-b577-862f0efca9f5","resolution":{"observed_at":"2026-08-07T11:24:21.902479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:21.674940Z","title":"Jointly optimized regressors for image super-resolution,","venue":null,"work_id":"f83db942-ff58-4ee4-96b0-54aa05b286c9","year":2015},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:16.249002Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:99c52e2e736523b20822595d10e50c7df4fa5029551d9ab4fb84019c94b6ac2e","observation_id":"c8bd78be-f737-4d15-803b-464220b0e957","resolution":{"observed_at":"2026-08-07T11:24:21.744851Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:21.580023Z","title":"Fast and accurate image upscaling with super-resolution forests,","venue":null,"work_id":"621dc210-0831-4282-b079-4a35c4bf9bbb","year":2015},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:16.363396Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:30bc20cd92dc066d567e7038d2badefbeb93015bf93830dfb4cfc8f5eb365251","observation_id":"51a30947-8ffa-43ff-b330-6740868e752d","resolution":{"observed_at":"2026-08-07T11:24:21.610005Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:21.459793Z","title":"Deep networks for image super-resolution with sparse prior,","venue":null,"work_id":"33de1b58-fa41-47ba-8d2e-dfa4f7dcb85b","year":2015},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:16.472480Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:d2f4a5f40f04cbd3d3797a920da5fd477fd7269e146cd7c0eb22cc6f24dc198b","observation_id":"10eefbb0-8bcd-4da5-806b-1c428da66c9b","resolution":{"observed_at":"2026-08-07T11:24:21.511575Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:21.369284Z","title":"Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connec- tions,","venue":null,"work_id":"f61b7f01-ec1f-491c-8680-fbfe4b047f89","year":2016},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:16.574594Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:81872cbe55d5fd8d1f0c74d6ea5b1a3ff0656e7a147d34ed3d51f4d790c3c7b5","observation_id":"daa7da4f-9de5-40be-8fdb-5014af4904fc","resolution":{"observed_at":"2026-08-07T11:24:21.419732Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:21.265808Z","title":"Single image super-resolution with non-local means and steering kernel regression,","venue":null,"work_id":"b5806d12-a64d-4776-828b-ac763d044acb","year":2012},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:16.648722Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:ec06297a513145bfe7c9bdd2d16a9f9bfeacf327ed3cb2e6210883461b21ece0","observation_id":"79facb97-b892-487f-bf9f-5a197c955502","resolution":{"observed_at":"2026-08-07T11:24:21.304870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:21.155964Z","title":"Trainable nonlinear reaction diffusion: A flexible framework for fast and effective image restoration,","venue":null,"work_id":"390d4e0a-a24c-4316-b928-f9ca7ee834ab","year":2016},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:16.729530Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:c638904f99cd13450e9bc2e6e3636ea393e93190366111a343122bf2606998c5","observation_id":"88f0a55c-83d8-4125-83e9-38de60bc58e2","resolution":{"observed_at":"2026-08-07T11:24:21.194915Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:21.066668Z","title":"Fast single image super-resolution via dilated residual networks,","venue":null,"work_id":"ac71ffb7-c9e6-484c-993d-1432747deae4","year":2018},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:16.874902Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:e7f6251c271521616d906a5f968582da68b098344ef38fbc68e912104db32852","observation_id":"ea7eb2b6-df1a-4ca2-ae91-d874ed42e2b0","resolution":{"observed_at":"2026-08-07T11:24:21.104920Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:20.960330Z","title":"Structure-preserving image super-resolution via contextualized multitask learning,","venue":null,"work_id":"b7acfb8c-7331-4ad7-995f-d3c4444a38c2","year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:16.961861Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:0412712a2f5685e1bb98505a996acd361674ba78dd55ca5b04fac697176695b2","observation_id":"38285499-feb4-47a9-8dbf-c0c4fd6e6422","resolution":{"observed_at":"2026-08-07T11:24:20.997391Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:20.859076Z","title":"Image super resolution based on fusing multiple convolution neural networks,","venue":null,"work_id":"6b4ac1de-95fe-4312-aa92-2aa3aa5dee6c","year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.030222Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:cb6981a20684dd0ea4354a7c58ff6bb7d4547e917a8221b201f988ec59f15dcb","observation_id":"96aa7995-24c1-4e63-bfc3-639d594e94e3","resolution":{"observed_at":"2026-08-07T11:24:20.904509Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:20.768169Z","title":"Beyond deep residual learning for image restoration: Persistent homology-guided manifold simplification,","venue":null,"work_id":"bdefe246-2b2b-4c30-8853-d582ef5c08d5","year":2017},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.102117Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:dca63e45de1e546d53525d93975b43975d89d5f58d27b4e53820e2bc0ec96c36","observation_id":"35d482dc-c35f-4cc8-858d-1a61e15b226d","resolution":{"observed_at":"2026-08-07T11:24:20.814201Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:20.652522Z","title":"Self-learning super- resolution using convolutional principal component analysis and random matching,","venue":null,"work_id":"b82dd9a7-cb77-4e20-964c-e6f2bb53b6af","year":2018},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.224095Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:6a21ffe00b8c6ccacb8de56df7ff87a86b6263e608b59b40c6e1eb0fad60b91c","observation_id":"6668373a-9153-4a43-855a-ec2ab378a258","resolution":{"observed_at":"2026-08-07T11:24:20.698823Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:20.530898Z","title":"New architecture of deep recursive convolution networks for super-resolution,","venue":null,"work_id":"f90d4484-bc8e-490d-b147-124596bfa058","year":2019},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.309495Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:1183cc9f9d909c64ded7bd4fb25fd69446e74db1bc465091262808580f0eae8a","observation_id":"ab820245-f3aa-414a-b792-3a9cf0a4bb92","resolution":{"observed_at":"2026-08-07T11:24:20.593296Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:20.390294Z","title":"Lightweight image super-resolution with enhanced cnn,","venue":null,"work_id":"5b3b9468-6a90-4877-baff-2ebfa078908f","year":2020},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.404148Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:dbe581ecb7fd3c78f7f2727cffd698877f587261431d975d360f1645549a4639","observation_id":"055458e0-6bd7-4b3a-be9b-42b7ed0f258d","resolution":{"observed_at":"2026-08-07T11:24:20.465880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:20.248512Z","title":"A dual cnn for image super-resolution,","venue":null,"work_id":"5db346d4-b12e-4a02-9c78-ce7c46a2aec9","year":2022},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.483880Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:dae408669a4de732ca165532f6a68532ee346fa2c841b167e75336660cc8f3b5","observation_id":"95baccec-0874-49cd-adca-aac86842191b","resolution":{"observed_at":"2026-08-07T11:24:20.313143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:20.082518Z","title":"Unfolding the alternating optimization for blind super resolution,","venue":null,"work_id":"2bb206ac-5fe8-406a-8b13-ed6b84890a57","year":2020},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.539407Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:98e3d7854e249e4675f28bfd0014ed920a2b909c59aee55da5ded88c38a94d81","observation_id":"439dad49-02cf-4fdd-893e-da4c097118fb","resolution":{"observed_at":"2026-08-07T11:24:20.189102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:19.932365Z","title":"Deep constrained least squares for blind image super-resolution,","venue":null,"work_id":"dbb7e260-fcfb-40af-9770-b60e9a687fe2","year":2022},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.677905Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:04fd533b4f3f7ca447bfe0b1b1d9b064b7b6eb539b3ff117c8c530b7eee628bc","observation_id":"15c92df5-ba1e-4ec5-8845-56fef5c8a77c","resolution":{"observed_at":"2026-08-07T11:24:20.030673Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:19.669126Z","title":"Structure-aware deep networks and pixel- level generative adversarial training for single image super-resolution,","venue":null,"work_id":"20bb4013-55b5-4ef6-910d-8325f9180904","year":2023},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.760006Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:41b41ca390c8aceda8dbc578092f6f625ff9e0bf5dbe3b7d753691576bcece96","observation_id":"f4a44df9-d7c1-43aa-a02a-ed50775be542","resolution":{"observed_at":"2026-08-07T11:24:19.789773Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:19.480269Z","title":"Image super-resolution via lightweight attention-directed feature aggregation network,","venue":null,"work_id":"337ec652-4203-4d43-9a09-0350fe01ff58","year":2023},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.834498Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:5d308b9881a154d2667843d37dcfa3fbe69dad360ec1e51cfb78967bd9d4daa0","observation_id":"ff0b5d48-f9fe-4675-9278-2d88a5db29fe","resolution":{"observed_at":"2026-08-07T11:24:19.553965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:19.268283Z","title":"Acdmsr: Accelerated conditional diffusion models for single image super-resolution,","venue":null,"work_id":"a7818438-683d-49bf-919e-083ea11fa085","year":2024},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:17.870561Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:c3aa3537e81a1182a61a90df7fd8be6b2fdc33d1499d227f69bd3922cc26ee39","observation_id":"b2dc6634-80a6-4e05-ab5c-e14235adbad4","resolution":{"observed_at":"2026-08-07T11:24:19.387559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:19.094588Z","title":"Cross-resolution feature attention network for image super-resolution,","venue":null,"work_id":"c9dc0947-df27-495e-b749-34736d0ff406","year":2023},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:18.022785Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:f05d28b62f399c82746e5be97224a837225b762c2129fc73657dcda702aa2117","observation_id":"f64784d7-e605-477b-a836-1ba0c5691303","resolution":{"observed_at":"2026-08-07T11:24:19.171375Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.17133","last_updated":"2025-02-12T06:47:37Z","snapshot_observed_at":"2026-07-06T17:35:53.644734Z","submitted_at":"2024-02-27T01:57:02Z","title":"SAM-DiffSR: Structure-Modulated Diffusion Model for Image Super-Resolution","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.17133","snapshot_observed_at":"2026-08-07T11:24:18.115055Z","title":"Sam- diffsr: Structure-modulated diffusion model for image super-resolution,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:18.115055Z"},"links":{"cited_paper":"/paper/2402.17133","citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:b24bcb54d7f2bb0a90e8046653fef489df77aa9b83917d88c5faa488c126fa45","observation_id":"0d795fef-3053-4b59-838f-6c2b6f4335fb","resolution":{"observed_at":"2026-08-07T11:24:18.115055Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:18.209853Z","title":"Image quality metrics: Psnr vs. ssim,","venue":null,"work_id":null,"year":2010},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:18.209853Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:c7166de5d34660a6f0564edbae4770eafe66149c73438fdf28d3c0615ae32d89","observation_id":"69f0ad5d-9645-4572-872a-44e171d2c053","resolution":{"observed_at":"2026-08-07T11:24:18.209853Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:18.298123Z","title":"Image quality assessment: from error visibility to structural similarity,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:18.298123Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:df17b40e4b063f6084b0f95633d2b57df72babc997a698c7c7e0bc1d47f8bafc","observation_id":"d84fe333-2663-4fbf-a8ad-1e8b8be35bea","resolution":{"observed_at":"2026-08-07T11:24:18.298123Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:18.867484Z","title":"Deep Learning Face Attributes in the Wild,","venue":null,"work_id":"ba69b2a0-6800-47f5-aef2-a8448f033d06","year":2015},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:18.403799Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:27a95996db4ee754d9afdb30fc4581eec7fe76a14bbcd1e24e80b5f918de2cde","observation_id":"21e267bf-0e8d-4558-93f4-182cd81dc5f8","resolution":{"observed_at":"2026-08-07T11:24:18.998614Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T11:24:18.646851Z","title":"The PASCAL Visual Object Classes Challenge: A Retrospective,","venue":null,"work_id":"2207fd5b-2e40-41a9-be71-0100e076eec1","year":2015},"citing_paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-07T11:24:18.492893Z"},"links":{"citing_paper":"/paper/2506.02585"},"observation_digest":"sha256:e48c7a4f65e9169a77892eee217c7d89253a9dd8bf0e8058a78c755aae391a4e","observation_id":"bf009b5c-0cc0-4ec3-954e-bd563b42a768","resolution":{"observed_at":"2026-08-07T11:24:18.715559Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.02585","last_updated":"2025-06-03T08:05:11Z","latest_version":1,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-07T11:18:26.681357Z","submitted_at":"2025-06-03T08:05:11Z","title":"A Tree-guided CNN for image super-resolution"},"reference_resolution":{"displayed":73,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":19,"verified_exact":0,"verified_fuzzy":54},"total_outbound_references":73},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:2506.02585."}