{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:LFMZYJXDMUKJ76BY3S74K5AVCO","short_pith_number":"pith:LFMZYJXD","schema_version":"1.0","canonical_sha256":"59599c26e365149ff838dcbfc5741513ae40ed36d6d4d85ef75841c86c448202","source":{"kind":"arxiv","id":"1612.02583","version":1},"attestation_state":"computed","paper":{"title":"From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anton van den Hengel, Chunhua Shen, Dong Gong, Ian Reid, Jie Yang, Lingqiao Liu, Qinfeng Shi, Yanning Zhang","submitted_at":"2016-12-08T10:05:57Z","abstract_excerpt":"Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but the extensive literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling all possible image content. The critical observation underpinning our approach is thus that learning the motion flow instead allo"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1612.02583","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T10:05:57Z","cross_cats_sorted":[],"title_canon_sha256":"000b52d459bdba3a2124517013472cd06bf53fd64656408388bc1ba8692ee630","abstract_canon_sha256":"dcba35f70a8894a88ae21caee07d7d13b9cf867696a6ae1c529e193cc2f90259"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:55:33.329894Z","signature_b64":"XZHwyN3RqBTuhaYYaclhphbnN3vqbGjwMUCnmEZAWjvuWmtj3amRCWpm65n8exnKq+NmXyN75u+sZxI5L/D2Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59599c26e365149ff838dcbfc5741513ae40ed36d6d4d85ef75841c86c448202","last_reissued_at":"2026-05-18T00:55:33.329209Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:55:33.329209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anton van den Hengel, Chunhua Shen, Dong Gong, Ian Reid, Jie Yang, Lingqiao Liu, Qinfeng Shi, Yanning Zhang","submitted_at":"2016-12-08T10:05:57Z","abstract_excerpt":"Removing pixel-wise heterogeneous motion blur is challenging due to the ill-posed nature of the problem. The predominant solution is to estimate the blur kernel by adding a prior, but the extensive literature on the subject indicates the difficulty in identifying a prior which is suitably informative, and general. Rather than imposing a prior based on theory, we propose instead to learn one from the data. Learning a prior over the latent image would require modeling all possible image content. The critical observation underpinning our approach is thus that learning the motion flow instead allo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02583","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1612.02583","created_at":"2026-05-18T00:55:33.329330+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.02583v1","created_at":"2026-05-18T00:55:33.329330+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02583","created_at":"2026-05-18T00:55:33.329330+00:00"},{"alias_kind":"pith_short_12","alias_value":"LFMZYJXDMUKJ","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_16","alias_value":"LFMZYJXDMUKJ76BY","created_at":"2026-05-18T12:30:29.479603+00:00"},{"alias_kind":"pith_short_8","alias_value":"LFMZYJXD","created_at":"2026-05-18T12:30:29.479603+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO","json":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO.json","graph_json":"https://pith.science/api/pith-number/LFMZYJXDMUKJ76BY3S74K5AVCO/graph.json","events_json":"https://pith.science/api/pith-number/LFMZYJXDMUKJ76BY3S74K5AVCO/events.json","paper":"https://pith.science/paper/LFMZYJXD"},"agent_actions":{"view_html":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO","download_json":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO.json","view_paper":"https://pith.science/paper/LFMZYJXD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.02583&json=true","fetch_graph":"https://pith.science/api/pith-number/LFMZYJXDMUKJ76BY3S74K5AVCO/graph.json","fetch_events":"https://pith.science/api/pith-number/LFMZYJXDMUKJ76BY3S74K5AVCO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/action/storage_attestation","attest_author":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/action/author_attestation","sign_citation":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/action/citation_signature","submit_replication":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/action/replication_record"}},"created_at":"2026-05-18T00:55:33.329330+00:00","updated_at":"2026-05-18T00:55:33.329330+00:00"}