{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:LFMZYJXDMUKJ76BY3S74K5AVCO","short_pith_number":"pith:LFMZYJXD","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"},"canonical_sha256":"59599c26e365149ff838dcbfc5741513ae40ed36d6d4d85ef75841c86c448202","source":{"kind":"arxiv","id":"1612.02583","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.02583","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"arxiv_version","alias_value":"1612.02583v1","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02583","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"pith_short_12","alias_value":"LFMZYJXDMUKJ","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LFMZYJXDMUKJ76BY","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LFMZYJXD","created_at":"2026-05-18T12:30:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:LFMZYJXDMUKJ76BY3S74K5AVCO","target":"record","payload":{"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"},"canonical_sha256":"59599c26e365149ff838dcbfc5741513ae40ed36d6d4d85ef75841c86c448202","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"},"source_kind":"arxiv","source_id":"1612.02583","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":"2026-05-18T00:55:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"joAWjHB7Qq38lEzGt1/PTPW1F7KgPNbudve2BkrPorJKb7syjbg/uM1ejTWpHVaaGaK8v96KZMcrHp63zOoaDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T19:39:53.097207Z"},"content_sha256":"463f1482b52c565955136d67b6be6073ea75a149f2fc0a1db8a07c48cf9c72c2","schema_version":"1.0","event_id":"sha256:463f1482b52c565955136d67b6be6073ea75a149f2fc0a1db8a07c48cf9c72c2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:LFMZYJXDMUKJ76BY3S74K5AVCO","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:55:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dVG9lX8PsD3Kw3FyvrvrU/Vp0SeYVUexmtU98Ni5NY8Eu5cD5OcfFvOvSJI+/M8/XCS57fcmoUlcAtcoaGBwCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T19:39:53.097860Z"},"content_sha256":"59f4cc87e68bdf445b57baba4665e48781f674f4e3464ebb2c82b6c624578d29","schema_version":"1.0","event_id":"sha256:59f4cc87e68bdf445b57baba4665e48781f674f4e3464ebb2c82b6c624578d29"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/bundle.json","state_url":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/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":"2026-05-26T19:39:53Z","links":{"resolver":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO","bundle":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/bundle.json","state":"https://pith.science/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LFMZYJXDMUKJ76BY3S74K5AVCO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:LFMZYJXDMUKJ76BY3S74K5AVCO","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":"dcba35f70a8894a88ae21caee07d7d13b9cf867696a6ae1c529e193cc2f90259","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T10:05:57Z","title_canon_sha256":"000b52d459bdba3a2124517013472cd06bf53fd64656408388bc1ba8692ee630"},"schema_version":"1.0","source":{"id":"1612.02583","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1612.02583","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"arxiv_version","alias_value":"1612.02583v1","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02583","created_at":"2026-05-18T00:55:33Z"},{"alias_kind":"pith_short_12","alias_value":"LFMZYJXDMUKJ","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_16","alias_value":"LFMZYJXDMUKJ76BY","created_at":"2026-05-18T12:30:29Z"},{"alias_kind":"pith_short_8","alias_value":"LFMZYJXD","created_at":"2026-05-18T12:30:29Z"}],"graph_snapshots":[{"event_id":"sha256:59f4cc87e68bdf445b57baba4665e48781f674f4e3464ebb2c82b6c624578d29","target":"graph","created_at":"2026-05-18T00:55:33Z","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":"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","authors_text":"Anton van den Hengel, Chunhua Shen, Dong Gong, Ian Reid, Jie Yang, Lingqiao Liu, Qinfeng Shi, Yanning Zhang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T10:05:57Z","title":"From Motion Blur to Motion Flow: a Deep Learning Solution for Removing Heterogeneous Motion Blur"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02583","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":"sha256:463f1482b52c565955136d67b6be6073ea75a149f2fc0a1db8a07c48cf9c72c2","target":"record","created_at":"2026-05-18T00:55:33Z","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":"dcba35f70a8894a88ae21caee07d7d13b9cf867696a6ae1c529e193cc2f90259","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T10:05:57Z","title_canon_sha256":"000b52d459bdba3a2124517013472cd06bf53fd64656408388bc1ba8692ee630"},"schema_version":"1.0","source":{"id":"1612.02583","kind":"arxiv","version":1}},"canonical_sha256":"59599c26e365149ff838dcbfc5741513ae40ed36d6d4d85ef75841c86c448202","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"59599c26e365149ff838dcbfc5741513ae40ed36d6d4d85ef75841c86c448202","first_computed_at":"2026-05-18T00:55:33.329209Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:55:33.329209Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"XZHwyN3RqBTuhaYYaclhphbnN3vqbGjwMUCnmEZAWjvuWmtj3amRCWpm65n8exnKq+NmXyN75u+sZxI5L/D2Cg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:55:33.329894Z","signed_message":"canonical_sha256_bytes"},"source_id":"1612.02583","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:463f1482b52c565955136d67b6be6073ea75a149f2fc0a1db8a07c48cf9c72c2","sha256:59f4cc87e68bdf445b57baba4665e48781f674f4e3464ebb2c82b6c624578d29"],"state_sha256":"e01bf704c5d03abe0753caac2f20d56c988f1df3877d8da4727374a538d118a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kfnxA03d/Xnr36KU6NX+G4vIpr5eu/hvoL68myJAC/KM5oGDPV0BNO7O0MiKNJW/MfynqRx+2g1tz6cvzSuaDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T19:39:53.101368Z","bundle_sha256":"080bc4ba785156a4eb7d000201c1b1cc7cfd0035d1a67473dea77d1663446303"}}