{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:WB7CQR7MO3KCCELMGEZESSGIHD","short_pith_number":"pith:WB7CQR7M","canonical_record":{"source":{"id":"1801.05117","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-16T05:02:09Z","cross_cats_sorted":[],"title_canon_sha256":"612ce19cfacabd75f3320edc813fc3264154b9cbaa294686534e02790bbf0124","abstract_canon_sha256":"418fdfcfe82c9d227406d0c18160b5547e7b8ba861f2586524c94d7257eeaa55"},"schema_version":"1.0"},"canonical_sha256":"b07e2847ec76d421116c31324948c838c40917a92d622fd5774f3bff80bb6d71","source":{"kind":"arxiv","id":"1801.05117","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.05117","created_at":"2026-05-18T00:25:47Z"},{"alias_kind":"arxiv_version","alias_value":"1801.05117v1","created_at":"2026-05-18T00:25:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.05117","created_at":"2026-05-18T00:25:47Z"},{"alias_kind":"pith_short_12","alias_value":"WB7CQR7MO3KC","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"WB7CQR7MO3KCCELM","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"WB7CQR7M","created_at":"2026-05-18T12:32:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:WB7CQR7MO3KCCELMGEZESSGIHD","target":"record","payload":{"canonical_record":{"source":{"id":"1801.05117","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-16T05:02:09Z","cross_cats_sorted":[],"title_canon_sha256":"612ce19cfacabd75f3320edc813fc3264154b9cbaa294686534e02790bbf0124","abstract_canon_sha256":"418fdfcfe82c9d227406d0c18160b5547e7b8ba861f2586524c94d7257eeaa55"},"schema_version":"1.0"},"canonical_sha256":"b07e2847ec76d421116c31324948c838c40917a92d622fd5774f3bff80bb6d71","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:25:47.883674Z","signature_b64":"/Jhu3bwwcIb0hE8zKAxMz93sXFV2OosEKg/RKLrsENzGDD7UTb+d/FzWWsYVo6KB8SEx+iL6pjqhaPBXM/vDDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b07e2847ec76d421116c31324948c838c40917a92d622fd5774f3bff80bb6d71","last_reissued_at":"2026-05-18T00:25:47.882942Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:25:47.882942Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1801.05117","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":"2026-05-18T00:25:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tvXBh0nxG8eK4WGYFd4ARk+o+P3/Qf3FBiTZrA43/8c32pfjEqqcCwE/LPbcf+JWUCo6CNty2kZshRwZUxSYBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:15:22.546702Z"},"content_sha256":"65aae76c8df102413b68c029618693030645f07b20c698cacc3c2f7fdc3bfbe1","schema_version":"1.0","event_id":"sha256:65aae76c8df102413b68c029618693030645f07b20c698cacc3c2f7fdc3bfbe1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:WB7CQR7MO3KCCELMGEZESSGIHD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reblur2Deblur: Deblurring Videos via Self-Supervised Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ashok Veeraraghavan, Huaijin Chen, Jan Kautz, Jinwei Gu, Ming-Yu Liu, Orazio Gallo","submitted_at":"2018-01-16T05:02:09Z","abstract_excerpt":"Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image formation model and use hand-crafted priors: they usually produce results that better reflect the underlying scene, but present artifacts. Recent learning-based methods implicitly extract the distribution of natural images directly from the data and use it to synthesize plausible images. Their results are impressive, but they are not always faithful to the content of the latent image. We present an approach that bridges t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.05117","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":"2026-05-18T00:25:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b71GsemAOET5NEV3Di+YTi45F4GVzrXKYdGR9qO2vcxj9CdLunI/Fjkg4DEjrGTtAo89TeXHFkwas5XwknTYAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T22:15:22.547379Z"},"content_sha256":"1060da30071a95659e4f63bd6424540add3f14ca49883b1920d79451f3a4c7ab","schema_version":"1.0","event_id":"sha256:1060da30071a95659e4f63bd6424540add3f14ca49883b1920d79451f3a4c7ab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WB7CQR7MO3KCCELMGEZESSGIHD/bundle.json","state_url":"https://pith.science/pith/WB7CQR7MO3KCCELMGEZESSGIHD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WB7CQR7MO3KCCELMGEZESSGIHD/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":"2026-05-25T22:15:22Z","links":{"resolver":"https://pith.science/pith/WB7CQR7MO3KCCELMGEZESSGIHD","bundle":"https://pith.science/pith/WB7CQR7MO3KCCELMGEZESSGIHD/bundle.json","state":"https://pith.science/pith/WB7CQR7MO3KCCELMGEZESSGIHD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WB7CQR7MO3KCCELMGEZESSGIHD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:WB7CQR7MO3KCCELMGEZESSGIHD","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":"418fdfcfe82c9d227406d0c18160b5547e7b8ba861f2586524c94d7257eeaa55","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-16T05:02:09Z","title_canon_sha256":"612ce19cfacabd75f3320edc813fc3264154b9cbaa294686534e02790bbf0124"},"schema_version":"1.0","source":{"id":"1801.05117","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1801.05117","created_at":"2026-05-18T00:25:47Z"},{"alias_kind":"arxiv_version","alias_value":"1801.05117v1","created_at":"2026-05-18T00:25:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.05117","created_at":"2026-05-18T00:25:47Z"},{"alias_kind":"pith_short_12","alias_value":"WB7CQR7MO3KC","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_16","alias_value":"WB7CQR7MO3KCCELM","created_at":"2026-05-18T12:32:59Z"},{"alias_kind":"pith_short_8","alias_value":"WB7CQR7M","created_at":"2026-05-18T12:32:59Z"}],"graph_snapshots":[{"event_id":"sha256:1060da30071a95659e4f63bd6424540add3f14ca49883b1920d79451f3a4c7ab","target":"graph","created_at":"2026-05-18T00:25:47Z","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":"Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image formation model and use hand-crafted priors: they usually produce results that better reflect the underlying scene, but present artifacts. Recent learning-based methods implicitly extract the distribution of natural images directly from the data and use it to synthesize plausible images. Their results are impressive, but they are not always faithful to the content of the latent image. We present an approach that bridges t","authors_text":"Ashok Veeraraghavan, Huaijin Chen, Jan Kautz, Jinwei Gu, Ming-Yu Liu, Orazio Gallo","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-16T05:02:09Z","title":"Reblur2Deblur: Deblurring Videos via Self-Supervised Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.05117","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":"sha256:65aae76c8df102413b68c029618693030645f07b20c698cacc3c2f7fdc3bfbe1","target":"record","created_at":"2026-05-18T00:25:47Z","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":"418fdfcfe82c9d227406d0c18160b5547e7b8ba861f2586524c94d7257eeaa55","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-16T05:02:09Z","title_canon_sha256":"612ce19cfacabd75f3320edc813fc3264154b9cbaa294686534e02790bbf0124"},"schema_version":"1.0","source":{"id":"1801.05117","kind":"arxiv","version":1}},"canonical_sha256":"b07e2847ec76d421116c31324948c838c40917a92d622fd5774f3bff80bb6d71","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b07e2847ec76d421116c31324948c838c40917a92d622fd5774f3bff80bb6d71","first_computed_at":"2026-05-18T00:25:47.882942Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:25:47.882942Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/Jhu3bwwcIb0hE8zKAxMz93sXFV2OosEKg/RKLrsENzGDD7UTb+d/FzWWsYVo6KB8SEx+iL6pjqhaPBXM/vDDA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:25:47.883674Z","signed_message":"canonical_sha256_bytes"},"source_id":"1801.05117","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:65aae76c8df102413b68c029618693030645f07b20c698cacc3c2f7fdc3bfbe1","sha256:1060da30071a95659e4f63bd6424540add3f14ca49883b1920d79451f3a4c7ab"],"state_sha256":"03847a049eba6a5304d4ad7dbdc0e3def64679755dbca12c3c9803aa8ede5b8d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nCJTEAchJGGMf2HtBYZLfWEidD4hKBL6uG/GB9TI5O9U0PsxcHqVN0CoVtpXCUwdExd6qyERDPglAX9kk4HTBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T22:15:22.551394Z","bundle_sha256":"f0f1d929527f85195c9310d01af3ba18d3804d2dec053ad8ef8f4c41c5ceafce"}}