{"as_of":"2026-08-13T05:18:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a9947b5ed6feeb4efa37c06bab0b6012896d103687d7f243b2a32a16760e40ee","coverage":[{"denominator":47,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":47,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T13:47:49.327612Z","state":"measured"},{"denominator":47,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":47,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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/2411.15921/citation-record","integrity":"/paper/2411.15921/integrity","json":"/paper/2411.15921/citation-record.json","paper":"/paper/2411.15921"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:47:49.944516Z","title":"Interferometric synthetic aperture radar (sar) missions employing formation flying,","venue":null,"work_id":"114b10d1-4a20-46a8-b33f-5d90a5614aa0","year":2010},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.132983Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:5dbd28c673577ddb755c1021c88eedc7fb1a58f6ff57d705a2c150b7c4187391","observation_id":"99381d60-fc1c-474d-8c44-9ac2b0c670f9","resolution":{"observed_at":"2026-08-12T13:47:49.948894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.930673Z","title":"Learning a dilated residual network for sar image despeckling,","venue":null,"work_id":"a82a6df2-dbb1-4456-9e50-982a866ad2ed","year":2018},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.137931Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:1820c11ecf472e14dc6406f9ee03e6ec3f7adeb77ee5da2d230f5908d97bff28","observation_id":"1b6ce413-fe55-421f-9347-cca437fc9491","resolution":{"observed_at":"2026-08-12T13:47:49.935136Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.916413Z","title":"Polarimetric-spatial classification of sar images based on the fusion of multiple classifiers,","venue":null,"work_id":"b7eeed53-bc50-46ff-9f63-e4af87d83ea4","year":2013},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.142249Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:c4ef63263b6500066068768f5b1579adaedea232f74948934fa0a8c1aeac6870","observation_id":"b5e0ed32-2a8c-4801-9ddb-12d3517abaf9","resolution":{"observed_at":"2026-08-12T13:47:49.921529Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.146983Z","title":"Some fundamental properties of speckle,","venue":null,"work_id":null,"year":1976},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.146983Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:c990ea3d1fe3fd6fa264bd4e72de3f71c24f50cf5659dbea1dc8e336c65ce7b4","observation_id":"c0b45b78-703b-4eb8-b361-505281d908d9","resolution":{"observed_at":"2026-08-12T13:47:49.146983Z","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":"2026-08-12T13:47:49.894132Z","title":"Digital image enhancement and noise filtering by use of local statistics,","venue":null,"work_id":"86c267f5-5a2d-4eb2-bec9-3b145ad0c85b","year":1980},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.151414Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:e73ecfc11db4325fe73c17f0d86ef16b7322e1cc65840debe92e157f1bb751fc","observation_id":"bba0df88-b47f-4076-90f6-4038328070e0","resolution":{"observed_at":"2026-08-12T13:47:49.898761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.880679Z","title":"Adaptive noise smoothing filter for images with signal-dependent noise,","venue":null,"work_id":"47631152-e3f9-46df-9651-c93d3a0ad4f2","year":1985},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.155705Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:e883138729c56762bf6bd41ec28062adb9f2688bb7955c2772232e1d1b772436","observation_id":"9d2ca770-bf6b-4663-9200-088bb3fa27db","resolution":{"observed_at":"2026-08-12T13:47:49.885143Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.867186Z","title":"A model for radar images and its application to adaptive digital filtering of multiplicative noise,","venue":null,"work_id":"02b16e72-5f80-42b5-9bc9-3c4c2a562ac8","year":1982},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.160365Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:26f0a8d8fe5f34e4fa22b90739cb5414449764468955131e25f50187ba675201","observation_id":"39bd522d-b082-4f83-b240-7a053069d38e","resolution":{"observed_at":"2026-08-12T13:47:49.871637Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.853927Z","title":"Maximum a posteriori speckle filtering and first order texture models in sar images,","venue":null,"work_id":"5b27c060-b67f-46f9-9557-6cc72dcefa40","year":1990},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.164643Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:2efa2384f56f42bd4f14e182b7405ed362e80788106179689da2c72b6d8fde9f","observation_id":"8b6fada2-6acc-4c34-9089-fed6351f42a4","resolution":{"observed_at":"2026-08-12T13:47:49.858287Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.840499Z","title":"A variational approach to removing multi- plicative noise,","venue":null,"work_id":"bac9c839-0469-4235-8d67-6254d9fc6d4a","year":2008},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.168836Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:61df3c32eceda470fd4583e2d3255872226a6a7889bb777e3f362f650c77f4c3","observation_id":"27bb5879-c0d6-4edf-9d1f-c84f91299839","resolution":{"observed_at":"2026-08-12T13:47:49.844766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.827212Z","title":"A nonlinear inverse scale space method for a convex multiplicative noise model,","venue":null,"work_id":"b9562ea7-1c02-4b4b-ae32-79f88ecf5483","year":2008},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.173061Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:31f0d76991374e68d07e2f8ecd15c0d2b30e84c55b8eb9c9961e6161f9c67141","observation_id":"24704c22-ccf0-4e79-a18b-79f7f27c4f66","resolution":{"observed_at":"2026-08-12T13:47:49.831606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.812380Z","title":"A convex adaptive total variation model based on the gray level indicator for multiplicative noise removal,","venue":null,"work_id":"479696da-6839-470e-b22e-bd2a122c3c4f","year":2013},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.177713Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:c956b795130c84b2146e862aa122209e758d54193cc314d2e6228c8e70efbf57","observation_id":"61997403-cf8a-4753-af4b-bdcf13f954b8","resolution":{"observed_at":"2026-08-12T13:47:49.817563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.797887Z","title":"A dou- bly degenerate diffusion model based on the gray level indicator for multiplicative noise removal,","venue":null,"work_id":"bb0e42f7-22da-4ada-8bb2-47d1d4972053","year":2014},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.182138Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:b5034642e446d781cd66421cc66fa34fd32755568329439ada4d6503c4658bef","observation_id":"6b6844c2-aa35-4e36-80dc-62fd179b6379","resolution":{"observed_at":"2026-08-12T13:47:49.802480Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.784556Z","title":"Image denoising based on a variable spatially exponent pde,","venue":null,"work_id":"82a4b4a2-38e4-4729-b0b7-ecf030bb6247","year":2024},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.186382Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:7fd6588b2ce35b6acd29075fe32dec5106d0eb0760045330aa03fc340cb51f8d","observation_id":"fe2b02c0-bc23-4410-9076-c77e107bc79d","resolution":{"observed_at":"2026-08-12T13:47:49.788948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.770899Z","title":"Multiplicative noise removal for texture images based on adaptive anisotropic fractional diffusion equations,","venue":null,"work_id":"2ca9eaa8-c1eb-4d1b-ac3c-acf45ae70c2e","year":2019},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.190593Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:dc1c19f680302519c7e5deacb3ee1f2f0a6b9a90250e53ba068f013202f07c9c","observation_id":"a785b11a-3e73-4ced-8e25-b1db3bc049e9","resolution":{"observed_at":"2026-08-12T13:47:49.775261Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.757455Z","title":"Multiplicative noise removal based on the smooth diffusion equation,","venue":null,"work_id":"42eded9d-8141-48ee-bc94-5e0687a86f86","year":2019},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.194759Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:c58e8da02f6c18332d29eac8eddbd9c7aadcd7386eb0b4995dbf182ffb919405","observation_id":"059eeb08-001b-4148-a4c0-0c55e179a071","resolution":{"observed_at":"2026-08-12T13:47:49.761807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.743742Z","title":"Sar image despeckling through convolutional neural networks,","venue":null,"work_id":"ac975166-6278-4661-9349-d62728ab0d5b","year":2017},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.198753Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:371cd64f5bb3349a5e5372d78ea9512e0086bf971a462b679a781c3fb586f61e","observation_id":"f4676fa6-8bfd-473b-bb5b-4fbb73153bf5","resolution":{"observed_at":"2026-08-12T13:47:49.748333Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.202947Z","title":"Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.202947Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:183544a71b5db25791c3b2a48088b3a20950705e18e28f145f3263f3f9ae4bea","observation_id":"5fab20f5-fd16-4aaa-8ec9-ebdaa46d444a","resolution":{"observed_at":"2026-08-12T13:47:49.202947Z","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":"2026-08-12T13:47:49.207054Z","title":"Sar image despeckling using a convolutional neural network,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.207054Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:5499fe51a2e7b312d7a2f6a2c9a91da6f0228004f751c6522212e163deb3c068","observation_id":"332294cd-234b-44bd-9abb-11a30faa9d49","resolution":{"observed_at":"2026-08-12T13:47:49.207054Z","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":"2026-08-12T13:47:49.711657Z","title":"Sar image despeckling using plug-and-play admm,","venue":null,"work_id":"4bd19493-0bbf-49f1-8540-226974af2ff9","year":2020},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.211042Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:fee79115c66bfb3aa66c91b82fe57c1b5569743b2fb014a7197522e678d2550d","observation_id":"f5ea8e42-1c71-472b-92ab-f45878ca37c0","resolution":{"observed_at":"2026-08-12T13:47:49.716427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.696074Z","title":"Robustness to spatially-correlated speckle in plug-and-play polsar despeckling,","venue":null,"work_id":"3cb35098-a27f-410c-b27b-a8ee77a524d3","year":2024},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.215263Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:180f64d923972d94c6b0239dce800b92ef89c43ff4c00dbbd78b481b3c688dab","observation_id":"78fb949c-edde-49a4-bed9-69111c275d34","resolution":{"observed_at":"2026-08-12T13:47:49.701491Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.681592Z","title":"Plug-and-play priors enabled sar image inpainting in the presence of speckle noise,","venue":null,"work_id":"08201863-408c-40e2-b36b-4e9748d2f50a","year":2020},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.219358Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:ccd580f55042cf2828809f34883d248d29f1c4f47a0a5d7657e1f7354415dc1f","observation_id":"58865aca-9253-483c-9c94-f6691cf742b9","resolution":{"observed_at":"2026-08-12T13:47:49.686160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.667588Z","title":"Coupling model-and data-driven methods for remote sensing image restoration and fusion: Improving physical interpretability,","venue":null,"work_id":"034658b5-149d-4d5c-876e-18fd31699d89","year":2022},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.223405Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:efb549682a2fb05a11c2fee55748e9d07db94047943c0f96b6dda79ebb04798b","observation_id":"f0364b46-1ae6-4dde-b290-a32ce8a250c3","resolution":{"observed_at":"2026-08-12T13:47:49.672187Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.653909Z","title":"Sar image despeckling employing a recursive deep cnn prior,","venue":null,"work_id":"3d68f612-91b5-453f-b394-ff90b5ea775d","year":2020},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.227560Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:427151e4c87fe316caf15e40eb065a51a688c364aa61d9bab0396cb26aaec1ec","observation_id":"5ec60f85-ca6f-4e61-878e-f91914ac4561","resolution":{"observed_at":"2026-08-12T13:47:49.658574Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.640296Z","title":"Deep un- rolling network for sar image despeckling,","venue":null,"work_id":"2a27c9ff-2328-465f-82ee-3206cd3b3bdf","year":2024},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.231627Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:b91048351759190156c530e465261786cbd21363825b4c26a6d7811da86f66bd","observation_id":"818a3a89-f0bf-4aba-9b8d-3d86ad775e68","resolution":{"observed_at":"2026-08-12T13:47:49.645056Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.626337Z","title":"A comprehen- sive study on the robustness of deep learning-based image classification and object detection in remote sensing: Surveying and benchmarking,","venue":null,"work_id":"d0e16db4-2da8-4ad0-b569-690b4d18e8e3","year":2024},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.235855Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:58a9a642fe7ba0c0ccfe1058398244ed2ed710bac995f6fa2fbf652ad67f5c95","observation_id":"8a2a3dd3-5548-4a81-bb31-32e79030ddfd","resolution":{"observed_at":"2026-08-12T13:47:49.630962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-08-12T17:13:46.394331Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-12T13:47:49.239996Z","title":"Explaining and harnessing adversarial examples,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.239996Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:a411834d7a689e35c721e6be14970c91c06de739c364eeb777703caa7fde9401","observation_id":"85f85ea2-352a-4f12-a692-bc0149338351","resolution":{"observed_at":"2026-08-12T13:47:49.239996Z","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":"2026-08-12T13:47:49.244300Z","title":"Adversarial examples in the physical world,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.244300Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:37c27ea6beb63da9cd5029065308fc46b473987575f577c079fdeb4898802f4b","observation_id":"2e647bd8-075e-42ec-89a8-ecd3346e5e8c","resolution":{"observed_at":"2026-08-12T13:47:49.244300Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.16050","last_updated":"2023-07-07T02:40:02Z","snapshot_observed_at":"2026-07-06T15:47:42.066252Z","submitted_at":"2023-06-28T09:30:59Z","title":"Evaluating Similitude and Robustness of Deep Image Denoising Models via Adversarial Attack","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.16050","snapshot_observed_at":"2026-08-12T13:47:49.248513Z","title":"Evaluating similitude and robustness of deep image denoising models via adversarial attack,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.248513Z"},"links":{"cited_paper":"/paper/2306.16050","citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:8cb98ba1406ef95373d04b0f5daefe76642e425da123ee7faa0c466261797855","observation_id":"5b4950c4-11fb-43ac-8d0d-453f787c939d","resolution":{"observed_at":"2026-08-12T13:47:49.248513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2201.04397","last_updated":"2022-01-13T06:00:04Z","snapshot_observed_at":"2026-07-06T12:26:43.939660Z","submitted_at":"2022-01-12T10:23:14Z","title":"Towards Adversarially Robust Deep Image Denoising","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2201.04397","snapshot_observed_at":"2026-08-12T13:47:49.252905Z","title":"Towards adver- sarially robust deep image denoising,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.252905Z"},"links":{"cited_paper":"/paper/2201.04397","citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:393cae80258b7a0d6ec63e502d47a5e44f54e1f9a6a4e5bcbb164693d893969d","observation_id":"dd121aac-a4b5-46a8-87cc-e4d4a6573a03","resolution":{"observed_at":"2026-08-12T13:47:49.252905Z","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":"2026-08-12T13:47:49.257109Z","title":"Agsdnet: Attention and gradient-based sar denoising network,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.257109Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:b1b359c5fca639d68e0475694adbee19a030f11d823632c639daa51552075e0e","observation_id":"61862020-d82f-4f04-8274-079fd788f77b","resolution":{"observed_at":"2026-08-12T13:47:49.257109Z","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":"2026-08-12T13:47:49.596359Z","title":"Towards deep learning models resistant to adversarial attacks,","venue":null,"work_id":"9961a9ae-1fe6-4d94-b27a-5cb5413776ed","year":2017},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.261107Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:24c27c8a6c9b0ffe1f1ef992722f3e3ee58037a034899b43bccc8007ccb16bfa","observation_id":"f9d26376-2ff0-439f-9761-9f90a2192e84","resolution":{"observed_at":"2026-08-12T13:47:49.600743Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.581825Z","title":"Adversarial training for free!","venue":null,"work_id":"8bb9ec45-6e5b-4847-b88f-d3adb3f51922","year":2019},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.265082Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:b153efb94f5b1adfbb32d4db9a15a46cd8430c9d361c4fa2ee7ac6d94f03e089","observation_id":"d80813fa-bb02-4a25-b489-5e4ca0d28a25","resolution":{"observed_at":"2026-08-12T13:47:49.586072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1808.09540","last_updated":"2019-09-12T02:40:39Z","snapshot_observed_at":"2026-07-06T06:57:57.583279Z","submitted_at":"2018-08-28T20:53:01Z","title":"Lipschitz regularized Deep Neural Networks generalize and are adversarially robust","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.09540","snapshot_observed_at":"2026-08-12T13:47:49.269104Z","title":"Lipschitz regularized deep neural networks converge and generalize,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.269104Z"},"links":{"cited_paper":"/paper/1808.09540","citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:fe6ddaa7d2c59a60927b9509a877c092673ca9f45d184da08eb452e23e16411d","observation_id":"dd70150a-5a7b-4657-8070-5ab2c0e96d1e","resolution":{"observed_at":"2026-08-12T13:47:49.269104Z","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":"2026-08-12T13:47:49.273325Z","title":"Clip: Cheap lipschitz training of neural networks,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.273325Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:e9af8a7fc76fe54487db7caab8fd513d4e07facd22f4585dff062e0c35aaceb7","observation_id":"e29cf0ab-104c-46ad-8cc8-0f270f680d4f","resolution":{"observed_at":"2026-08-12T13:47:49.273325Z","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":"2026-08-12T13:47:49.277318Z","title":"Lipschitz regularity of deep neural networks: analysis and efficient estimation,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.277318Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:29d9f491a73fbcab1ae561c8bc48de6d9cb856deb5c502cc6fe0aa3255931e27","observation_id":"5ea143e0-c5f8-452b-a412-658062a12922","resolution":{"observed_at":"2026-08-12T13:47:49.277318Z","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":"2026-08-12T13:47:49.550950Z","title":"A diffusion equation for improving the robustness of deep learning speckle removal model,","venue":null,"work_id":"fa854bd1-a4ab-407a-a214-4ff3e8c6d7fe","year":2024},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.281658Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:a814b7b25700df4c922cdc356d2863a04b57d654f1cc564cc442b1a6dafd2fa2","observation_id":"55b2f7ee-c8f0-4249-b772-c84cfe6f80eb","resolution":{"observed_at":"2026-08-12T13:47:49.555532Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6199","last_updated":"2014-02-19T16:33:14Z","snapshot_observed_at":"2026-07-06T03:31:33.797310Z","submitted_at":"2013-12-21T03:36:08Z","title":"Intriguing properties of neural networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6199","snapshot_observed_at":"2026-08-12T13:47:49.285780Z","title":"Intriguing properties of neural networks,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.285780Z"},"links":{"cited_paper":"/paper/1312.6199","citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:78b396b97d43195f9298226e677445dc0145684d46e478971757bed840875f40","observation_id":"ff60beab-13d5-4d17-93a1-3b45b07ae3ea","resolution":{"observed_at":"2026-08-12T13:47:49.285780Z","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":"2026-08-12T13:47:49.290342Z","title":null,"venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.290342Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:c8e5f407d1eca673427ac3d30bc97fcee6fd1b73c77bb89fdaa36d5f2404491d","observation_id":"55d506bc-4a8c-4bd7-a6c6-cb5eef145b7e","resolution":{"observed_at":"2026-08-12T13:47:49.290342Z","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":"2026-08-12T13:47:49.528023Z","title":"Weickert et al., Anisotropic diffusion in image processing","venue":null,"work_id":"9ac1b299-047f-4d7f-a752-8eaef64d69cf","year":1998},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.294311Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:3388accd3a514709af44e1d3cef95ba4e02e3706cc0d79d4bca95a8e34610d59","observation_id":"306c12b2-92be-4f24-9f5e-a2d17360f015","resolution":{"observed_at":"2026-08-12T13:47:49.532654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.512916Z","title":"A fast algorithm for euler’s elastica model using augmented lagrangian method,","venue":null,"work_id":"a42cd55d-5af0-41e7-b8ba-14edf6f90e58","year":2011},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.298427Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:77c4a00dc1812b2ee6fbc9556d5f436c82fe49d72ab0cda8bb0cd4509f3ef2f7","observation_id":"3cbb521e-de27-4b79-b558-07e23ec97d92","resolution":{"observed_at":"2026-08-12T13:47:49.518309Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.498926Z","title":"On single image scale-up using sparse-representations,","venue":null,"work_id":"c7c216c0-682c-4c93-837f-07b68840c126","year":2010},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.302409Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:afc7ff7b12d409074dd6eaa0d8279669a230e4447e0fd2169181ffb3784fb761","observation_id":"b89f8d57-8b2b-4f6f-9faa-156aae139ae5","resolution":{"observed_at":"2026-08-12T13:47:49.503407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:47:49.484264Z","title":"Color demosaicking by local directional interpolation and nonlocal adaptive thresholding,","venue":null,"work_id":"b304a868-3bc2-4394-be34-fc75bf144251","year":2011},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.306381Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:a7363cc13bb16a14c3de2e0b7393001a420ab2eebf789ba3b53c770f47c90823","observation_id":"6899ed40-392d-48ac-a19b-3379ccb7b446","resolution":{"observed_at":"2026-08-12T13:47:49.489571Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2012.02951","last_updated":"2020-12-05T05:15:36Z","snapshot_observed_at":"2026-08-09T04:58:04.040865Z","submitted_at":"2020-12-05T05:15:36Z","title":"FloodNet: A High Resolution Aerial Imagery Dataset for Post Flood Scene Understanding","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2012.02951","snapshot_observed_at":"2026-08-12T13:47:49.310498Z","title":"Floodnet: A high resolution aerial imagery dataset for post flood scene understanding,","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.310498Z"},"links":{"cited_paper":"/paper/2012.02951","citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:38f0fa189c24b29fd235e891e0d3c03d51fbb4ae09e72f3e8e0ae7599264700d","observation_id":"1a9ddb29-bc91-4dd4-9526-6b7074356561","resolution":{"observed_at":"2026-08-12T13:47:49.310498Z","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":"2026-08-12T13:47:49.314948Z","title":"Remote sensing image scene classifi- cation: Benchmark and state of the art,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.314948Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:6ed29655490f404d743b03b46ffcc4f7f21ed11552a9fbd835a37020946792a8","observation_id":"9d9f494f-462a-4844-8a5d-5bfe53996cb8","resolution":{"observed_at":"2026-08-12T13:47:49.314948Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1502.03167","last_updated":"2015-03-02T20:44:12Z","snapshot_observed_at":"2026-08-12T21:01:12.961874Z","submitted_at":"2015-02-11T01:44:18Z","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.03167","snapshot_observed_at":"2026-08-12T13:47:49.319179Z","title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.319179Z"},"links":{"cited_paper":"/paper/1502.03167","citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:5e7e03cebd08e77ef06cc58e63bd72fb834d256277579d1ab71758abacfb4e66","observation_id":"5e3a5497-6060-4998-9e94-9c7056b34b78","resolution":{"observed_at":"2026-08-12T13:47:49.319179Z","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":"2026-08-12T13:47:49.323579Z","title":"Mulog, or how to apply gaussian denoisers to multi-channel sar speckle reduction?","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.323579Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:77c65e518a5d3282d78895744d04e365124d24c0c0db494d867899ad3209c449","observation_id":"f9fe102b-df9e-46b1-9563-e1f779b37400","resolution":{"observed_at":"2026-08-12T13:47:49.323579Z","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":"2026-08-12T13:47:49.451174Z","title":"Transformer-based sar image despeckling,","venue":null,"work_id":"14f8489f-2fe9-46ba-9291-81297874c513","year":2022},"citing_paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-12T13:47:49.327612Z"},"links":{"citing_paper":"/paper/2411.15921"},"observation_digest":"sha256:5669c2dacd38a5b4d221234e5de7860166b4e9da05d6d3d69929033e5a64cff0","observation_id":"99d1034d-cca7-4998-af06-62a38f1cc363","resolution":{"observed_at":"2026-08-12T13:47:49.457280Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.15921","last_updated":"2024-12-23T16:50:54Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-12T13:42:04.795725Z","submitted_at":"2024-11-24T17:08:43Z","title":"A Tunable Despeckling Neural Network Stabilized via Diffusion Equation"},"reference_resolution":{"displayed":47,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":17,"verified_exact":0,"verified_fuzzy":30},"total_outbound_references":47},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 0 inbound Pith citation observations for arXiv:2411.15921."}