{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WZQXZO5SG4T4HT2J2RYXZFF5NY","short_pith_number":"pith:WZQXZO5S","canonical_record":{"source":{"id":"1802.03769","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-11T16:57:50Z","cross_cats_sorted":[],"title_canon_sha256":"c395db112dbbd43cd96e7c6a8e638bf22a0ccb28afcfe6b0c42b5e56b0aae0de","abstract_canon_sha256":"84205dd9210a03e89ccb9543bd1d51c5e0c8be944c762878d1e1a75e98ebf991"},"schema_version":"1.0"},"canonical_sha256":"b6617cbbb23727c3cf49d4717c94bd6e010fecc3d9cba1597bab1e1ac96765b7","source":{"kind":"arxiv","id":"1802.03769","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.03769","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"arxiv_version","alias_value":"1802.03769v1","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.03769","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"pith_short_12","alias_value":"WZQXZO5SG4T4","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WZQXZO5SG4T4HT2J","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WZQXZO5S","created_at":"2026-05-18T12:33:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WZQXZO5SG4T4HT2J2RYXZFF5NY","target":"record","payload":{"canonical_record":{"source":{"id":"1802.03769","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-11T16:57:50Z","cross_cats_sorted":[],"title_canon_sha256":"c395db112dbbd43cd96e7c6a8e638bf22a0ccb28afcfe6b0c42b5e56b0aae0de","abstract_canon_sha256":"84205dd9210a03e89ccb9543bd1d51c5e0c8be944c762878d1e1a75e98ebf991"},"schema_version":"1.0"},"canonical_sha256":"b6617cbbb23727c3cf49d4717c94bd6e010fecc3d9cba1597bab1e1ac96765b7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:50.473561Z","signature_b64":"PGvX7BJ8OxZCZi2N54jHIdd/ys8xyqkSZe1+tIXVdE7HsB/reqKZNGZAT736rtqP6MacMhi4Mx0Aq33+fQnIDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6617cbbb23727c3cf49d4717c94bd6e010fecc3d9cba1597bab1e1ac96765b7","last_reissued_at":"2026-05-18T00:23:50.472993Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:50.472993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.03769","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":"2026-05-18T00:23:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n3Ge4RLEX8aowFTz10P2ruLcA+Xkg1RlbdggLpSi7iKNVlPAScPocZQE9hn3bmEy9sI8wlGCcgJRhqg1GLVOAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T20:42:42.556208Z"},"content_sha256":"9a13568bd28d9ce6916022c8c078ad9da9b336cb208581096b02d0b232cfb2e5","schema_version":"1.0","event_id":"sha256:9a13568bd28d9ce6916022c8c078ad9da9b336cb208581096b02d0b232cfb2e5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WZQXZO5SG4T4HT2J2RYXZFF5NY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Deep Convolutional Networks for Demosaicing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Nai-Sheng Syu, Yung-Yu Chuang, Yu-Sheng Chen","submitted_at":"2018-02-11T16:57:50Z","abstract_excerpt":"This paper presents a comprehensive study of applying the convolutional neural network (CNN) to solving the demosaicing problem. The paper presents two CNN models that learn end-to-end mappings between the mosaic samples and the original image patches with full information. In the case the Bayer color filter array (CFA) is used, an evaluation with ten competitive methods on popular benchmarks confirms that the data-driven, automatically learned features by the CNN models are very effective. Experiments show that the proposed CNN models can perform equally well in both the sRGB space and the li"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.03769","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":"2026-05-18T00:23:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R9log7WsXDYzXWsxBFG2DLYISs1RpszpWflx5kK09e9K2jQsCtnbW3TGqGGGZeK148lQnZqmd1lAWMbVR3JsCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T20:42:42.556568Z"},"content_sha256":"61dd0c5ef0b26ec2677e60e02a3e52ec404bf9e63bf77e3cc56725f2b0b06f99","schema_version":"1.0","event_id":"sha256:61dd0c5ef0b26ec2677e60e02a3e52ec404bf9e63bf77e3cc56725f2b0b06f99"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WZQXZO5SG4T4HT2J2RYXZFF5NY/bundle.json","state_url":"https://pith.science/pith/WZQXZO5SG4T4HT2J2RYXZFF5NY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WZQXZO5SG4T4HT2J2RYXZFF5NY/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":"2026-07-02T20:42:42Z","links":{"resolver":"https://pith.science/pith/WZQXZO5SG4T4HT2J2RYXZFF5NY","bundle":"https://pith.science/pith/WZQXZO5SG4T4HT2J2RYXZFF5NY/bundle.json","state":"https://pith.science/pith/WZQXZO5SG4T4HT2J2RYXZFF5NY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WZQXZO5SG4T4HT2J2RYXZFF5NY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WZQXZO5SG4T4HT2J2RYXZFF5NY","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":"84205dd9210a03e89ccb9543bd1d51c5e0c8be944c762878d1e1a75e98ebf991","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-11T16:57:50Z","title_canon_sha256":"c395db112dbbd43cd96e7c6a8e638bf22a0ccb28afcfe6b0c42b5e56b0aae0de"},"schema_version":"1.0","source":{"id":"1802.03769","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.03769","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"arxiv_version","alias_value":"1802.03769v1","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.03769","created_at":"2026-05-18T00:23:50Z"},{"alias_kind":"pith_short_12","alias_value":"WZQXZO5SG4T4","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_16","alias_value":"WZQXZO5SG4T4HT2J","created_at":"2026-05-18T12:33:01Z"},{"alias_kind":"pith_short_8","alias_value":"WZQXZO5S","created_at":"2026-05-18T12:33:01Z"}],"graph_snapshots":[{"event_id":"sha256:61dd0c5ef0b26ec2677e60e02a3e52ec404bf9e63bf77e3cc56725f2b0b06f99","target":"graph","created_at":"2026-05-18T00:23:50Z","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":"This paper presents a comprehensive study of applying the convolutional neural network (CNN) to solving the demosaicing problem. The paper presents two CNN models that learn end-to-end mappings between the mosaic samples and the original image patches with full information. In the case the Bayer color filter array (CFA) is used, an evaluation with ten competitive methods on popular benchmarks confirms that the data-driven, automatically learned features by the CNN models are very effective. Experiments show that the proposed CNN models can perform equally well in both the sRGB space and the li","authors_text":"Nai-Sheng Syu, Yung-Yu Chuang, Yu-Sheng Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-11T16:57:50Z","title":"Learning Deep Convolutional Networks for Demosaicing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.03769","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":"sha256:9a13568bd28d9ce6916022c8c078ad9da9b336cb208581096b02d0b232cfb2e5","target":"record","created_at":"2026-05-18T00:23:50Z","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":"84205dd9210a03e89ccb9543bd1d51c5e0c8be944c762878d1e1a75e98ebf991","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-11T16:57:50Z","title_canon_sha256":"c395db112dbbd43cd96e7c6a8e638bf22a0ccb28afcfe6b0c42b5e56b0aae0de"},"schema_version":"1.0","source":{"id":"1802.03769","kind":"arxiv","version":1}},"canonical_sha256":"b6617cbbb23727c3cf49d4717c94bd6e010fecc3d9cba1597bab1e1ac96765b7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b6617cbbb23727c3cf49d4717c94bd6e010fecc3d9cba1597bab1e1ac96765b7","first_computed_at":"2026-05-18T00:23:50.472993Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:23:50.472993Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"PGvX7BJ8OxZCZi2N54jHIdd/ys8xyqkSZe1+tIXVdE7HsB/reqKZNGZAT736rtqP6MacMhi4Mx0Aq33+fQnIDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:23:50.473561Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.03769","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a13568bd28d9ce6916022c8c078ad9da9b336cb208581096b02d0b232cfb2e5","sha256:61dd0c5ef0b26ec2677e60e02a3e52ec404bf9e63bf77e3cc56725f2b0b06f99"],"state_sha256":"410562de9d6101c9ba7b5b4e87bda1a7bd62f547255e636851d718a6f3b19b60"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"MXbcrAyHVVmUy24A48tJMRgF5Jmtn/9hPZOyYLrk+A0VG5owOcyQESqHoSzy4i6u2yPS076r/I4V3Fxr28poAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T20:42:42.558531Z","bundle_sha256":"a4d7102b6823a268d16b1a1a34072dfffdd66739bc29fe6c576da242e3e57686"}}