{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:7H3MGZDROUV24LPDJIJ55DMNRP","short_pith_number":"pith:7H3MGZDR","canonical_record":{"source":{"id":"2212.04711","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-09T07:48:30Z","cross_cats_sorted":[],"title_canon_sha256":"94ad0a1d02936b7bc74732f7379ad70ffe2c8533764dcebeaf5d2c1a3ad97cd2","abstract_canon_sha256":"724217d45f306b8e207fde149d68e3a465c56724b06824c9d42a3088225d0da7"},"schema_version":"1.0"},"canonical_sha256":"f9f6c36471752bae2de34a13de8d8d8bcdd27abfe310922750f7053b91fc3509","source":{"kind":"arxiv","id":"2212.04711","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.04711","created_at":"2026-07-05T05:24:46Z"},{"alias_kind":"arxiv_version","alias_value":"2212.04711v2","created_at":"2026-07-05T05:24:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.04711","created_at":"2026-07-05T05:24:46Z"},{"alias_kind":"pith_short_12","alias_value":"7H3MGZDROUV2","created_at":"2026-07-05T05:24:46Z"},{"alias_kind":"pith_short_16","alias_value":"7H3MGZDROUV24LPD","created_at":"2026-07-05T05:24:46Z"},{"alias_kind":"pith_short_8","alias_value":"7H3MGZDR","created_at":"2026-07-05T05:24:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:7H3MGZDROUV24LPDJIJ55DMNRP","target":"record","payload":{"canonical_record":{"source":{"id":"2212.04711","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-09T07:48:30Z","cross_cats_sorted":[],"title_canon_sha256":"94ad0a1d02936b7bc74732f7379ad70ffe2c8533764dcebeaf5d2c1a3ad97cd2","abstract_canon_sha256":"724217d45f306b8e207fde149d68e3a465c56724b06824c9d42a3088225d0da7"},"schema_version":"1.0"},"canonical_sha256":"f9f6c36471752bae2de34a13de8d8d8bcdd27abfe310922750f7053b91fc3509","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:24:46.447031Z","signature_b64":"g/7+SoJAzFNASu1I0tZ2hn5wxSY65qUErQ4otEeWpYPGBoaLeakDylIdsHIrxu1fPwBplHsC59AvnGZLqrMgAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f9f6c36471752bae2de34a13de8d8d8bcdd27abfe310922750f7053b91fc3509","last_reissued_at":"2026-07-05T05:24:46.446635Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:24:46.446635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2212.04711","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T05:24:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9lxvlSDdzpc2VbqHUJvKOUDem1tTt+cqd7RIDYjCbhoj+m3TY1r6dxPEgo3CNUryMssgtVa50NobpVonyr4gBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:09:45.000330Z"},"content_sha256":"ab9adb671129c50df9574238f716170531a0d5c386ccf74e58bf914224a886a6","schema_version":"1.0","event_id":"sha256:ab9adb671129c50df9574238f716170531a0d5c386ccf74e58bf914224a886a6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:7H3MGZDROUV24LPDJIJ55DMNRP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bihan Wen, Chong Wang, Hanspeter Pfister, Lanqing Guo, Siyu Huang, Wenhan Yang, Yufei Wang","submitted_at":"2022-12-09T07:48:30Z","abstract_excerpt":"Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model'"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.04711","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2212.04711/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T05:24:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bTqTtP1/bbAFETe5Ni//2NUuSdWIKBsOf3gNKHMxG3D5CZ6QbVdZexiB5wMweJPpZdlR+9Gu3CTwWAL/KqTpAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T14:09:45.000693Z"},"content_sha256":"69cb639d707793c289f6f97570c6b6c22a758313eebd5361796e544125d499b7","schema_version":"1.0","event_id":"sha256:69cb639d707793c289f6f97570c6b6c22a758313eebd5361796e544125d499b7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7H3MGZDROUV24LPDJIJ55DMNRP/bundle.json","state_url":"https://pith.science/pith/7H3MGZDROUV24LPDJIJ55DMNRP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7H3MGZDROUV24LPDJIJ55DMNRP/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":"2026-07-07T14:09:45Z","links":{"resolver":"https://pith.science/pith/7H3MGZDROUV24LPDJIJ55DMNRP","bundle":"https://pith.science/pith/7H3MGZDROUV24LPDJIJ55DMNRP/bundle.json","state":"https://pith.science/pith/7H3MGZDROUV24LPDJIJ55DMNRP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7H3MGZDROUV24LPDJIJ55DMNRP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:7H3MGZDROUV24LPDJIJ55DMNRP","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":"724217d45f306b8e207fde149d68e3a465c56724b06824c9d42a3088225d0da7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-09T07:48:30Z","title_canon_sha256":"94ad0a1d02936b7bc74732f7379ad70ffe2c8533764dcebeaf5d2c1a3ad97cd2"},"schema_version":"1.0","source":{"id":"2212.04711","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2212.04711","created_at":"2026-07-05T05:24:46Z"},{"alias_kind":"arxiv_version","alias_value":"2212.04711v2","created_at":"2026-07-05T05:24:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2212.04711","created_at":"2026-07-05T05:24:46Z"},{"alias_kind":"pith_short_12","alias_value":"7H3MGZDROUV2","created_at":"2026-07-05T05:24:46Z"},{"alias_kind":"pith_short_16","alias_value":"7H3MGZDROUV24LPD","created_at":"2026-07-05T05:24:46Z"},{"alias_kind":"pith_short_8","alias_value":"7H3MGZDR","created_at":"2026-07-05T05:24:46Z"}],"graph_snapshots":[{"event_id":"sha256:69cb639d707793c289f6f97570c6b6c22a758313eebd5361796e544125d499b7","target":"graph","created_at":"2026-07-05T05:24:46Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2212.04711/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent deep learning methods have achieved promising results in image shadow removal. However, their restored images still suffer from unsatisfactory boundary artifacts, due to the lack of degradation prior embedding and the deficiency in modeling capacity. Our work addresses these issues by proposing a unified diffusion framework that integrates both the image and degradation priors for highly effective shadow removal. In detail, we first propose a shadow degradation model, which inspires us to build a novel unrolling diffusion model, dubbed ShandowDiffusion. It remarkably improves the model'","authors_text":"Bihan Wen, Chong Wang, Hanspeter Pfister, Lanqing Guo, Siyu Huang, Wenhan Yang, Yufei Wang","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-09T07:48:30Z","title":"ShadowDiffusion: When Degradation Prior Meets Diffusion Model for Shadow Removal"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2212.04711","kind":"arxiv","version":2},"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:ab9adb671129c50df9574238f716170531a0d5c386ccf74e58bf914224a886a6","target":"record","created_at":"2026-07-05T05:24:46Z","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":"724217d45f306b8e207fde149d68e3a465c56724b06824c9d42a3088225d0da7","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2022-12-09T07:48:30Z","title_canon_sha256":"94ad0a1d02936b7bc74732f7379ad70ffe2c8533764dcebeaf5d2c1a3ad97cd2"},"schema_version":"1.0","source":{"id":"2212.04711","kind":"arxiv","version":2}},"canonical_sha256":"f9f6c36471752bae2de34a13de8d8d8bcdd27abfe310922750f7053b91fc3509","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f9f6c36471752bae2de34a13de8d8d8bcdd27abfe310922750f7053b91fc3509","first_computed_at":"2026-07-05T05:24:46.446635Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:24:46.446635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"g/7+SoJAzFNASu1I0tZ2hn5wxSY65qUErQ4otEeWpYPGBoaLeakDylIdsHIrxu1fPwBplHsC59AvnGZLqrMgAw==","signature_status":"signed_v1","signed_at":"2026-07-05T05:24:46.447031Z","signed_message":"canonical_sha256_bytes"},"source_id":"2212.04711","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ab9adb671129c50df9574238f716170531a0d5c386ccf74e58bf914224a886a6","sha256:69cb639d707793c289f6f97570c6b6c22a758313eebd5361796e544125d499b7"],"state_sha256":"78037d6c4aa1ef02cd126b3a39f7105c5afec76f8610ec937f6bb3521cf3cd85"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xEEKb6xwlSFffKnLbmikfXIn5bf/mJR0aBDFI+SovX3aGniQhEUCT1vyjPD5p7DWjtC7dArUcyFqYxqy7BZbDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T14:09:45.002732Z","bundle_sha256":"969e8fe428c98278abdd8d5731f0e675c7896afacbb46435e36fc42a35d4cd31"}}