{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:P4BSCTOCSYFRXTOP5M7YFOX265","short_pith_number":"pith:P4BSCTOC","canonical_record":{"source":{"id":"1510.02930","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-10-10T13:44:47Z","cross_cats_sorted":[],"title_canon_sha256":"2783106551f3769dd0734781586f93aab17dec7c1ba95dd29af44eef781acbd6","abstract_canon_sha256":"dc5650bbc7b555c22f73cf4365210fdedd7e64216f32458ad8f1e4433cfa7279"},"schema_version":"1.0"},"canonical_sha256":"7f03214dc2960b1bcdcfeb3f82bafaf7561d29aa3284760d8164fc7fa1416fcd","source":{"kind":"arxiv","id":"1510.02930","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.02930","created_at":"2026-05-18T01:30:33Z"},{"alias_kind":"arxiv_version","alias_value":"1510.02930v1","created_at":"2026-05-18T01:30:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.02930","created_at":"2026-05-18T01:30:33Z"},{"alias_kind":"pith_short_12","alias_value":"P4BSCTOCSYFR","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"P4BSCTOCSYFRXTOP","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"P4BSCTOC","created_at":"2026-05-18T12:29:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:P4BSCTOCSYFRXTOP5M7YFOX265","target":"record","payload":{"canonical_record":{"source":{"id":"1510.02930","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-10-10T13:44:47Z","cross_cats_sorted":[],"title_canon_sha256":"2783106551f3769dd0734781586f93aab17dec7c1ba95dd29af44eef781acbd6","abstract_canon_sha256":"dc5650bbc7b555c22f73cf4365210fdedd7e64216f32458ad8f1e4433cfa7279"},"schema_version":"1.0"},"canonical_sha256":"7f03214dc2960b1bcdcfeb3f82bafaf7561d29aa3284760d8164fc7fa1416fcd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:30:33.309181Z","signature_b64":"KK0oKFP9TtBHrjGFkHKXtg3dIdYWTjcEPkTyvSioc/54EpJvckZq8gS2tv6ardhoHfoTcc52xaI52MnNlIj5BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7f03214dc2960b1bcdcfeb3f82bafaf7561d29aa3284760d8164fc7fa1416fcd","last_reissued_at":"2026-05-18T01:30:33.308364Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:30:33.308364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1510.02930","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:30:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zb69dZ3ALdrtjLbNdxWVMFJ5/RvxMQidgYxjYQhb4vZgoIVgkJe3o2N47kWrpCsKrLYx+ffLMcPplUSvTyv7Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T18:00:15.152228Z"},"content_sha256":"38f461a75444e2e8844d23cc6e3fdf3c0f37834f9575644123716492c0e52cb5","schema_version":"1.0","event_id":"sha256:38f461a75444e2e8844d23cc6e3fdf3c0f37834f9575644123716492c0e52cb5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:P4BSCTOCSYFRXTOP5M7YFOX265","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Wensen Feng, Yunjin Chen","submitted_at":"2015-10-10T13:44:47Z","abstract_excerpt":"The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision and microscopy. Up to now, many state-of-the-art Poisson denoising techniques mainly concentrate on achieving utmost performance, with little consideration for the computation efficiency. Therefore, in this study we aim to propose an efficient Poisson denoising model with both high computational efficiency and recovery quality. To this end, we exploit the newly-developed trainable nonlinear reaction diffusion model which has proven an extremely fas"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.02930","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:30:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2Ot/32EbcLtGf5AcZH2yg+mPUkiZ7k5Pi9KN+J8YXV8HfCAPrVCpYcq1Pgz1yQoOsq+sWGz5aB+m+hVnNG2mBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-25T18:00:15.152586Z"},"content_sha256":"b3b2ffd19af49251cde2e995ae5eb02846166e6d75c7ba61aaef3ceb2b6f90cc","schema_version":"1.0","event_id":"sha256:b3b2ffd19af49251cde2e995ae5eb02846166e6d75c7ba61aaef3ceb2b6f90cc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P4BSCTOCSYFRXTOP5M7YFOX265/bundle.json","state_url":"https://pith.science/pith/P4BSCTOCSYFRXTOP5M7YFOX265/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P4BSCTOCSYFRXTOP5M7YFOX265/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-25T18:00:15Z","links":{"resolver":"https://pith.science/pith/P4BSCTOCSYFRXTOP5M7YFOX265","bundle":"https://pith.science/pith/P4BSCTOCSYFRXTOP5M7YFOX265/bundle.json","state":"https://pith.science/pith/P4BSCTOCSYFRXTOP5M7YFOX265/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P4BSCTOCSYFRXTOP5M7YFOX265/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:P4BSCTOCSYFRXTOP5M7YFOX265","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"dc5650bbc7b555c22f73cf4365210fdedd7e64216f32458ad8f1e4433cfa7279","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-10-10T13:44:47Z","title_canon_sha256":"2783106551f3769dd0734781586f93aab17dec7c1ba95dd29af44eef781acbd6"},"schema_version":"1.0","source":{"id":"1510.02930","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.02930","created_at":"2026-05-18T01:30:33Z"},{"alias_kind":"arxiv_version","alias_value":"1510.02930v1","created_at":"2026-05-18T01:30:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.02930","created_at":"2026-05-18T01:30:33Z"},{"alias_kind":"pith_short_12","alias_value":"P4BSCTOCSYFR","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"P4BSCTOCSYFRXTOP","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"P4BSCTOC","created_at":"2026-05-18T12:29:34Z"}],"graph_snapshots":[{"event_id":"sha256:b3b2ffd19af49251cde2e995ae5eb02846166e6d75c7ba61aaef3ceb2b6f90cc","target":"graph","created_at":"2026-05-18T01:30:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision and microscopy. Up to now, many state-of-the-art Poisson denoising techniques mainly concentrate on achieving utmost performance, with little consideration for the computation efficiency. Therefore, in this study we aim to propose an efficient Poisson denoising model with both high computational efficiency and recovery quality. To this end, we exploit the newly-developed trainable nonlinear reaction diffusion model which has proven an extremely fas","authors_text":"Wensen Feng, Yunjin Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-10-10T13:44:47Z","title":"Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.02930","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:38f461a75444e2e8844d23cc6e3fdf3c0f37834f9575644123716492c0e52cb5","target":"record","created_at":"2026-05-18T01:30:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"dc5650bbc7b555c22f73cf4365210fdedd7e64216f32458ad8f1e4433cfa7279","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2015-10-10T13:44:47Z","title_canon_sha256":"2783106551f3769dd0734781586f93aab17dec7c1ba95dd29af44eef781acbd6"},"schema_version":"1.0","source":{"id":"1510.02930","kind":"arxiv","version":1}},"canonical_sha256":"7f03214dc2960b1bcdcfeb3f82bafaf7561d29aa3284760d8164fc7fa1416fcd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7f03214dc2960b1bcdcfeb3f82bafaf7561d29aa3284760d8164fc7fa1416fcd","first_computed_at":"2026-05-18T01:30:33.308364Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:30:33.308364Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KK0oKFP9TtBHrjGFkHKXtg3dIdYWTjcEPkTyvSioc/54EpJvckZq8gS2tv6ardhoHfoTcc52xaI52MnNlIj5BQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:30:33.309181Z","signed_message":"canonical_sha256_bytes"},"source_id":"1510.02930","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:38f461a75444e2e8844d23cc6e3fdf3c0f37834f9575644123716492c0e52cb5","sha256:b3b2ffd19af49251cde2e995ae5eb02846166e6d75c7ba61aaef3ceb2b6f90cc"],"state_sha256":"c245c09144e95d636fd250ffa79c255fb4f5ad361874a145812e0f064be32b9c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kVC1eEaWS1bfoagFzApW4lCpf/1wpqbSjJZix+nNlGaF+hh4VblgIT+mQ3pcJzecDp29G5vTsUMU3VuuYNxXAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-25T18:00:15.154567Z","bundle_sha256":"3e7bb4be603bc0e550b55bd9bfaa6a1cde6979d066d65ae30ac876d502c4bece"}}