{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:MLLX7AFQHP7AWCQSWLE2LO2LKQ","short_pith_number":"pith:MLLX7AFQ","canonical_record":{"source":{"id":"1903.00107","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-02-28T23:21:30Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"5c1b9aa2d5dca1e76947da30c8739dd4344f2b5d446796b0c77e39d87a3f64a3","abstract_canon_sha256":"88e58c97d6d5e58cc2c5104a61517d3bb629a038a25d62d6853c9c858151fe72"},"schema_version":"1.0"},"canonical_sha256":"62d77f80b03bfe0b0a12b2c9a5bb4b543829888f62981f31f48f867c1beffe21","source":{"kind":"arxiv","id":"1903.00107","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00107","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00107v1","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00107","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"pith_short_12","alias_value":"MLLX7AFQHP7A","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"MLLX7AFQHP7AWCQS","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"MLLX7AFQ","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:MLLX7AFQHP7AWCQSWLE2LO2LKQ","target":"record","payload":{"canonical_record":{"source":{"id":"1903.00107","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-02-28T23:21:30Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"5c1b9aa2d5dca1e76947da30c8739dd4344f2b5d446796b0c77e39d87a3f64a3","abstract_canon_sha256":"88e58c97d6d5e58cc2c5104a61517d3bb629a038a25d62d6853c9c858151fe72"},"schema_version":"1.0"},"canonical_sha256":"62d77f80b03bfe0b0a12b2c9a5bb4b543829888f62981f31f48f867c1beffe21","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:52:20.902147Z","signature_b64":"9vNNwzp8xb08VR6wtimaVY4t8Umbj6WoS4E0orlup8lw9cvdI16ABzTJUAUFDvpNOKFKevUiDsGhsRgP+Pp2Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62d77f80b03bfe0b0a12b2c9a5bb4b543829888f62981f31f48f867c1beffe21","last_reissued_at":"2026-05-17T23:52:20.901435Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:52:20.901435Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.00107","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":"2026-05-17T23:52:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"P+fv8wqXrQlGfOz66n8EReHmUSMdktJDyGv78EuvsxKKPk+PfbaslWshk36XC3Js6UrDQMz4TA1ejO1RKZ1rBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T07:26:29.124361Z"},"content_sha256":"47c605af0e45a006dd9279a6325adb9c203390d20cd72099e27ffdf1cd0c4dc7","schema_version":"1.0","event_id":"sha256:47c605af0e45a006dd9279a6325adb9c203390d20cd72099e27ffdf1cd0c4dc7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:MLLX7AFQHP7AWCQSWLE2LO2LKQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GAN Based Image Deblurring Using Dark Channel Prior","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Ada Zhen, Robert L. Stevenson, Shuang Zhang","submitted_at":"2019-02-28T23:21:30Z","abstract_excerpt":"A conditional general adversarial network (GAN) is proposed for image deblurring problem. It is tailored for image deblurring instead of just applying GAN on the deblurring problem. Motivated by that, dark channel prior is carefully picked to be incorporated into the loss function for network training. To make it more compatible with neuron networks, its original indifferentiable form is discarded and L2 norm is adopted instead. On both synthetic datasets and noisy natural images, the proposed network shows improved deblurring performance and robustness to image noise qualitatively and quantit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00107","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":"2026-05-17T23:52:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JV10WUIVoVeQkXK/oUEDUDYBTbMemSIRqJEZo6Jx9w+A5jJLfDCMroVIrDMw2Md9KCa3C7XjArBujRJH5Uu2Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T07:26:29.125057Z"},"content_sha256":"cc106d1e04b6f640c69769350aa036a9fec46c714d2c1b2e6ab07fb6edb55c01","schema_version":"1.0","event_id":"sha256:cc106d1e04b6f640c69769350aa036a9fec46c714d2c1b2e6ab07fb6edb55c01"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MLLX7AFQHP7AWCQSWLE2LO2LKQ/bundle.json","state_url":"https://pith.science/pith/MLLX7AFQHP7AWCQSWLE2LO2LKQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MLLX7AFQHP7AWCQSWLE2LO2LKQ/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":"2026-05-27T07:26:29Z","links":{"resolver":"https://pith.science/pith/MLLX7AFQHP7AWCQSWLE2LO2LKQ","bundle":"https://pith.science/pith/MLLX7AFQHP7AWCQSWLE2LO2LKQ/bundle.json","state":"https://pith.science/pith/MLLX7AFQHP7AWCQSWLE2LO2LKQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MLLX7AFQHP7AWCQSWLE2LO2LKQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:MLLX7AFQHP7AWCQSWLE2LO2LKQ","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":"88e58c97d6d5e58cc2c5104a61517d3bb629a038a25d62d6853c9c858151fe72","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-02-28T23:21:30Z","title_canon_sha256":"5c1b9aa2d5dca1e76947da30c8739dd4344f2b5d446796b0c77e39d87a3f64a3"},"schema_version":"1.0","source":{"id":"1903.00107","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00107","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00107v1","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00107","created_at":"2026-05-17T23:52:20Z"},{"alias_kind":"pith_short_12","alias_value":"MLLX7AFQHP7A","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"MLLX7AFQHP7AWCQS","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"MLLX7AFQ","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:cc106d1e04b6f640c69769350aa036a9fec46c714d2c1b2e6ab07fb6edb55c01","target":"graph","created_at":"2026-05-17T23:52:20Z","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":"A conditional general adversarial network (GAN) is proposed for image deblurring problem. It is tailored for image deblurring instead of just applying GAN on the deblurring problem. Motivated by that, dark channel prior is carefully picked to be incorporated into the loss function for network training. To make it more compatible with neuron networks, its original indifferentiable form is discarded and L2 norm is adopted instead. On both synthetic datasets and noisy natural images, the proposed network shows improved deblurring performance and robustness to image noise qualitatively and quantit","authors_text":"Ada Zhen, Robert L. Stevenson, Shuang Zhang","cross_cats":["eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-02-28T23:21:30Z","title":"GAN Based Image Deblurring Using Dark Channel Prior"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00107","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":"sha256:47c605af0e45a006dd9279a6325adb9c203390d20cd72099e27ffdf1cd0c4dc7","target":"record","created_at":"2026-05-17T23:52:20Z","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":"88e58c97d6d5e58cc2c5104a61517d3bb629a038a25d62d6853c9c858151fe72","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2019-02-28T23:21:30Z","title_canon_sha256":"5c1b9aa2d5dca1e76947da30c8739dd4344f2b5d446796b0c77e39d87a3f64a3"},"schema_version":"1.0","source":{"id":"1903.00107","kind":"arxiv","version":1}},"canonical_sha256":"62d77f80b03bfe0b0a12b2c9a5bb4b543829888f62981f31f48f867c1beffe21","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"62d77f80b03bfe0b0a12b2c9a5bb4b543829888f62981f31f48f867c1beffe21","first_computed_at":"2026-05-17T23:52:20.901435Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:20.901435Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9vNNwzp8xb08VR6wtimaVY4t8Umbj6WoS4E0orlup8lw9cvdI16ABzTJUAUFDvpNOKFKevUiDsGhsRgP+Pp2Ag==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:20.902147Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.00107","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:47c605af0e45a006dd9279a6325adb9c203390d20cd72099e27ffdf1cd0c4dc7","sha256:cc106d1e04b6f640c69769350aa036a9fec46c714d2c1b2e6ab07fb6edb55c01"],"state_sha256":"d7125fb4480c86744d7ea82ac75cd045e9c33d59447fbf3d4a5345b8100bbf57"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FpBTz8wuAJ1Tb/YmkOjDKxYPU+IvrRK7D8i+MMDgA7vv+4njkPbJ675baIUfXaP9RZETthOZoNklMiY84Dt1BQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T07:26:29.128506Z","bundle_sha256":"fe5a940e5c2b92315d2e49f1b39a327178c32adea89cd2742dc32158b953b143"}}