{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:LPLJLBRZBW3VTEZXDMBPNVVAR2","short_pith_number":"pith:LPLJLBRZ","canonical_record":{"source":{"id":"1902.03493","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-02-09T21:19:35Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"415d07c2e96e2f788d8460388c4a69c238b4647c35eeb5b53bf02e9ea2f3cbd6","abstract_canon_sha256":"e8f31ac9205d92b7a939291c760a4664e30e5bd4dc815970fd3e500d0f90421a"},"schema_version":"1.0"},"canonical_sha256":"5bd69586390db75993371b02f6d6a08ea4ca1f097bcf7a190be5f1391f79fc39","source":{"kind":"arxiv","id":"1902.03493","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.03493","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"arxiv_version","alias_value":"1902.03493v3","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03493","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"pith_short_12","alias_value":"LPLJLBRZBW3V","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"LPLJLBRZBW3VTEZX","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"LPLJLBRZ","created_at":"2026-05-18T12:33:21Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:LPLJLBRZBW3VTEZXDMBPNVVAR2","target":"record","payload":{"canonical_record":{"source":{"id":"1902.03493","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-02-09T21:19:35Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"415d07c2e96e2f788d8460388c4a69c238b4647c35eeb5b53bf02e9ea2f3cbd6","abstract_canon_sha256":"e8f31ac9205d92b7a939291c760a4664e30e5bd4dc815970fd3e500d0f90421a"},"schema_version":"1.0"},"canonical_sha256":"5bd69586390db75993371b02f6d6a08ea4ca1f097bcf7a190be5f1391f79fc39","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:47.573612Z","signature_b64":"Mv8h3aUJuwxXgbsmIRwdFas2lUrWeGNSC7YYlEuUO5IU7YDu3JhjVdrx5tY95tu/YzL/WRF5kdqVPMv8kb+XAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5bd69586390db75993371b02f6d6a08ea4ca1f097bcf7a190be5f1391f79fc39","last_reissued_at":"2026-05-17T23:44:47.573045Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:47.573045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.03493","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":"2026-05-17T23:44:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+o3Ra5Smx1hj98fNI8q5ovogM5wJkkY7yLNnAq5UqbrVdTlMIlvBaM1hORzuzXiS7R3hqmWsHP5uRcAOboGrBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T23:04:57.301820Z"},"content_sha256":"fc767d472756bb1340ce59e671770067c18cfbbdb313e39b826a5e2be7b78d83","schema_version":"1.0","event_id":"sha256:fc767d472756bb1340ce59e671770067c18cfbbdb313e39b826a5e2be7b78d83"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:LPLJLBRZBW3VTEZXDMBPNVVAR2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Algorithm Unrolling for Blind Image Deblurring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.ML"],"primary_cat":"eess.IV","authors_text":"Junyi Geng, Mohammad Tofighi, Vishal Monga, Yonina C. Eldar, Yuelong Li","submitted_at":"2019-02-09T21:19:35Z","abstract_excerpt":"Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. However, such connections ha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03493","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":""},"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":"2026-05-17T23:44:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J24PXkgQhplqCov9BzKCLSmee5bqPKy3U64RTwkMrBvs+vCE7m9S34CyT6SqXW/pj4Kh3cf3BmSqc8j3el8OCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-31T23:04:57.302618Z"},"content_sha256":"7fe976474695ed96f197c84810df57a93695e27120060aa6ed555580dac32e0b","schema_version":"1.0","event_id":"sha256:7fe976474695ed96f197c84810df57a93695e27120060aa6ed555580dac32e0b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LPLJLBRZBW3VTEZXDMBPNVVAR2/bundle.json","state_url":"https://pith.science/pith/LPLJLBRZBW3VTEZXDMBPNVVAR2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LPLJLBRZBW3VTEZXDMBPNVVAR2/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":"2026-05-31T23:04:57Z","links":{"resolver":"https://pith.science/pith/LPLJLBRZBW3VTEZXDMBPNVVAR2","bundle":"https://pith.science/pith/LPLJLBRZBW3VTEZXDMBPNVVAR2/bundle.json","state":"https://pith.science/pith/LPLJLBRZBW3VTEZXDMBPNVVAR2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LPLJLBRZBW3VTEZXDMBPNVVAR2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:LPLJLBRZBW3VTEZXDMBPNVVAR2","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":"e8f31ac9205d92b7a939291c760a4664e30e5bd4dc815970fd3e500d0f90421a","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-02-09T21:19:35Z","title_canon_sha256":"415d07c2e96e2f788d8460388c4a69c238b4647c35eeb5b53bf02e9ea2f3cbd6"},"schema_version":"1.0","source":{"id":"1902.03493","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.03493","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"arxiv_version","alias_value":"1902.03493v3","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.03493","created_at":"2026-05-17T23:44:47Z"},{"alias_kind":"pith_short_12","alias_value":"LPLJLBRZBW3V","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_16","alias_value":"LPLJLBRZBW3VTEZX","created_at":"2026-05-18T12:33:21Z"},{"alias_kind":"pith_short_8","alias_value":"LPLJLBRZ","created_at":"2026-05-18T12:33:21Z"}],"graph_snapshots":[{"event_id":"sha256:7fe976474695ed96f197c84810df57a93695e27120060aa6ed555580dac32e0b","target":"graph","created_at":"2026-05-17T23:44:47Z","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":"Blind image deblurring remains a topic of enduring interest. Learning based approaches, especially those that employ neural networks have emerged to complement traditional model based methods and in many cases achieve vastly enhanced performance. That said, neural network approaches are generally empirically designed and the underlying structures are difficult to interpret. In recent years, a promising technique called algorithm unrolling has been developed that has helped connect iterative algorithms such as those for sparse coding to neural network architectures. However, such connections ha","authors_text":"Junyi Geng, Mohammad Tofighi, Vishal Monga, Yonina C. Eldar, Yuelong Li","cross_cats":["cs.CV","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-02-09T21:19:35Z","title":"Deep Algorithm Unrolling for Blind Image Deblurring"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.03493","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:fc767d472756bb1340ce59e671770067c18cfbbdb313e39b826a5e2be7b78d83","target":"record","created_at":"2026-05-17T23:44:47Z","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":"e8f31ac9205d92b7a939291c760a4664e30e5bd4dc815970fd3e500d0f90421a","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-02-09T21:19:35Z","title_canon_sha256":"415d07c2e96e2f788d8460388c4a69c238b4647c35eeb5b53bf02e9ea2f3cbd6"},"schema_version":"1.0","source":{"id":"1902.03493","kind":"arxiv","version":3}},"canonical_sha256":"5bd69586390db75993371b02f6d6a08ea4ca1f097bcf7a190be5f1391f79fc39","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5bd69586390db75993371b02f6d6a08ea4ca1f097bcf7a190be5f1391f79fc39","first_computed_at":"2026-05-17T23:44:47.573045Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:47.573045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Mv8h3aUJuwxXgbsmIRwdFas2lUrWeGNSC7YYlEuUO5IU7YDu3JhjVdrx5tY95tu/YzL/WRF5kdqVPMv8kb+XAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:47.573612Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.03493","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fc767d472756bb1340ce59e671770067c18cfbbdb313e39b826a5e2be7b78d83","sha256:7fe976474695ed96f197c84810df57a93695e27120060aa6ed555580dac32e0b"],"state_sha256":"9c829bc3c1ee2b42d28e8e49849ed8020fe4b0981b39f07f4752f7d2cb56a364"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jVmC7dfQTEu8URCrOLsPSPwQfHyNcmngRgN8W50RcaFf+elQPlQsIXSet7sH3w7JD5ZaOzMZj7IpEZYff+NsBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-31T23:04:57.306776Z","bundle_sha256":"afc374d1bd2fb5c1b313edb05759eafa4bef2f0b8d56c8ceb270ce1de8b1d113"}}