{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:E3ISQLH47KW7ROGXY5OG2IPALB","short_pith_number":"pith:E3ISQLH4","canonical_record":{"source":{"id":"1806.05229","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T19:11:19Z","cross_cats_sorted":[],"title_canon_sha256":"2de5ee5852b9b32794d0b19e415b11a914ca5538586d88deeb73bc7b45942b86","abstract_canon_sha256":"66ee685cac7e8bc9811c74278d9e3e4892728c9336009c69d1a1aa66df5fe5cb"},"schema_version":"1.0"},"canonical_sha256":"26d1282cfcfaadf8b8d7c75c6d21e05871775c8c35c9d78003d3f531b2311d40","source":{"kind":"arxiv","id":"1806.05229","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05229","created_at":"2026-07-05T00:25:07Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05229v3","created_at":"2026-07-05T00:25:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05229","created_at":"2026-07-05T00:25:07Z"},{"alias_kind":"pith_short_12","alias_value":"E3ISQLH47KW7","created_at":"2026-07-05T00:25:07Z"},{"alias_kind":"pith_short_16","alias_value":"E3ISQLH47KW7ROGX","created_at":"2026-07-05T00:25:07Z"},{"alias_kind":"pith_short_8","alias_value":"E3ISQLH4","created_at":"2026-07-05T00:25:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:E3ISQLH47KW7ROGXY5OG2IPALB","target":"record","payload":{"canonical_record":{"source":{"id":"1806.05229","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T19:11:19Z","cross_cats_sorted":[],"title_canon_sha256":"2de5ee5852b9b32794d0b19e415b11a914ca5538586d88deeb73bc7b45942b86","abstract_canon_sha256":"66ee685cac7e8bc9811c74278d9e3e4892728c9336009c69d1a1aa66df5fe5cb"},"schema_version":"1.0"},"canonical_sha256":"26d1282cfcfaadf8b8d7c75c6d21e05871775c8c35c9d78003d3f531b2311d40","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:25:07.365381Z","signature_b64":"XD/x8lc9DtW1g8DmK/UT+Yragvgv6KsjCOX57MtvohsmQ5A+1fNV5P68sf+xBU7lhCMeeXgsOZYh4Di5Ij5CBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26d1282cfcfaadf8b8d7c75c6d21e05871775c8c35c9d78003d3f531b2311d40","last_reissued_at":"2026-07-05T00:25:07.364920Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:25:07.364920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.05229","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:25:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d3JaKTwXm6mynij5IMvQNvLC/eOU3UkWXKw20C/cIjhIVwL2UoO2xXyxvrTFTsI6/7AZjb2jpMnAJJM+6qgQBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:13:49.262120Z"},"content_sha256":"2e05e8410fb44f2838bcdbd226b6b687af339775b992d8813f6e99e081563388","schema_version":"1.0","event_id":"sha256:2e05e8410fb44f2838bcdbd226b6b687af339775b992d8813f6e99e081563388"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:E3ISQLH47KW7ROGXY5OG2IPALB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ayan Chakrabarti, Zhihao Xia","submitted_at":"2018-06-13T19:11:19Z","abstract_excerpt":"Image denoising methods must effectively model, implicitly or explicitly, the vast diversity of patterns and textures that occur in natural images. This is challenging, even for modern methods that leverage deep neural networks trained to regress to clean images from noisy inputs. One recourse is to rely on \"internal\" image statistics, by searching for similar patterns within the input image itself. In this work, we propose a new method for natural image denoising that trains a deep neural network to determine whether patches in a noisy image input share common underlying patterns. Given a pai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05229","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1806.05229/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T00:25:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dbDc3Si17S/xIN29MHptkUiLIXr/dBxq2yjJTu+GNJVAM3pFZI7HmsuZZHJ+tQR0TFpqLyBdjR/nzm7g4TzHDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T23:13:49.262506Z"},"content_sha256":"a3bc801c020c37044052b23b0943acc49552685be9b23fd970682876a839f2ef","schema_version":"1.0","event_id":"sha256:a3bc801c020c37044052b23b0943acc49552685be9b23fd970682876a839f2ef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/E3ISQLH47KW7ROGXY5OG2IPALB/bundle.json","state_url":"https://pith.science/pith/E3ISQLH47KW7ROGXY5OG2IPALB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/E3ISQLH47KW7ROGXY5OG2IPALB/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":"2026-07-06T23:13:49Z","links":{"resolver":"https://pith.science/pith/E3ISQLH47KW7ROGXY5OG2IPALB","bundle":"https://pith.science/pith/E3ISQLH47KW7ROGXY5OG2IPALB/bundle.json","state":"https://pith.science/pith/E3ISQLH47KW7ROGXY5OG2IPALB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/E3ISQLH47KW7ROGXY5OG2IPALB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:E3ISQLH47KW7ROGXY5OG2IPALB","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":"66ee685cac7e8bc9811c74278d9e3e4892728c9336009c69d1a1aa66df5fe5cb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T19:11:19Z","title_canon_sha256":"2de5ee5852b9b32794d0b19e415b11a914ca5538586d88deeb73bc7b45942b86"},"schema_version":"1.0","source":{"id":"1806.05229","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.05229","created_at":"2026-07-05T00:25:07Z"},{"alias_kind":"arxiv_version","alias_value":"1806.05229v3","created_at":"2026-07-05T00:25:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.05229","created_at":"2026-07-05T00:25:07Z"},{"alias_kind":"pith_short_12","alias_value":"E3ISQLH47KW7","created_at":"2026-07-05T00:25:07Z"},{"alias_kind":"pith_short_16","alias_value":"E3ISQLH47KW7ROGX","created_at":"2026-07-05T00:25:07Z"},{"alias_kind":"pith_short_8","alias_value":"E3ISQLH4","created_at":"2026-07-05T00:25:07Z"}],"graph_snapshots":[{"event_id":"sha256:a3bc801c020c37044052b23b0943acc49552685be9b23fd970682876a839f2ef","target":"graph","created_at":"2026-07-05T00:25:07Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1806.05229/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Image denoising methods must effectively model, implicitly or explicitly, the vast diversity of patterns and textures that occur in natural images. This is challenging, even for modern methods that leverage deep neural networks trained to regress to clean images from noisy inputs. One recourse is to rely on \"internal\" image statistics, by searching for similar patterns within the input image itself. In this work, we propose a new method for natural image denoising that trains a deep neural network to determine whether patches in a noisy image input share common underlying patterns. Given a pai","authors_text":"Ayan Chakrabarti, Zhihao Xia","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T19:11:19Z","title":"Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.05229","kind":"arxiv","version":3},"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":"sha256:2e05e8410fb44f2838bcdbd226b6b687af339775b992d8813f6e99e081563388","target":"record","created_at":"2026-07-05T00:25:07Z","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":"66ee685cac7e8bc9811c74278d9e3e4892728c9336009c69d1a1aa66df5fe5cb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-06-13T19:11:19Z","title_canon_sha256":"2de5ee5852b9b32794d0b19e415b11a914ca5538586d88deeb73bc7b45942b86"},"schema_version":"1.0","source":{"id":"1806.05229","kind":"arxiv","version":3}},"canonical_sha256":"26d1282cfcfaadf8b8d7c75c6d21e05871775c8c35c9d78003d3f531b2311d40","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"26d1282cfcfaadf8b8d7c75c6d21e05871775c8c35c9d78003d3f531b2311d40","first_computed_at":"2026-07-05T00:25:07.364920Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:25:07.364920Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XD/x8lc9DtW1g8DmK/UT+Yragvgv6KsjCOX57MtvohsmQ5A+1fNV5P68sf+xBU7lhCMeeXgsOZYh4Di5Ij5CBA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:25:07.365381Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.05229","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2e05e8410fb44f2838bcdbd226b6b687af339775b992d8813f6e99e081563388","sha256:a3bc801c020c37044052b23b0943acc49552685be9b23fd970682876a839f2ef"],"state_sha256":"eb931abf9707a862f17bf31f2cca49fbf7005d549c7a84c2f3d5f085068a03cd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fqmcorxgqxd/Ge/clnO+7bhKXEJusf+XAxgLKA/47vOHgmRDFY7Pf+/Vxiv7OlVGh0JMa72o6Tn6cbIs0o4kBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T23:13:49.264432Z","bundle_sha256":"72a4c5b8ed948c4a071c64b6f2ebe70822fbaa45f8ea77b79f53e9a08a57c5cb"}}