{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:6QEKBZPIBJF6XZRCEPDVCH24QK","short_pith_number":"pith:6QEKBZPI","canonical_record":{"source":{"id":"2305.12966","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-22T12:18:20Z","cross_cats_sorted":[],"title_canon_sha256":"2b1e518fe7b118ba1da61473ab33cd8fe889d25ed088ca917df0e0830f6c76db","abstract_canon_sha256":"309235101b408e98c1db1e107f049c40606495752130aaa5e0c4418467627575"},"schema_version":"1.0"},"canonical_sha256":"f408a0e5e80a4bebe62223c7511f5c828a13bf3f0a8290d297422df57410be0f","source":{"kind":"arxiv","id":"2305.12966","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.12966","created_at":"2026-07-05T06:53:37Z"},{"alias_kind":"arxiv_version","alias_value":"2305.12966v4","created_at":"2026-07-05T06:53:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12966","created_at":"2026-07-05T06:53:37Z"},{"alias_kind":"pith_short_12","alias_value":"6QEKBZPIBJF6","created_at":"2026-07-05T06:53:37Z"},{"alias_kind":"pith_short_16","alias_value":"6QEKBZPIBJF6XZRC","created_at":"2026-07-05T06:53:37Z"},{"alias_kind":"pith_short_8","alias_value":"6QEKBZPI","created_at":"2026-07-05T06:53:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:6QEKBZPIBJF6XZRCEPDVCH24QK","target":"record","payload":{"canonical_record":{"source":{"id":"2305.12966","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-22T12:18:20Z","cross_cats_sorted":[],"title_canon_sha256":"2b1e518fe7b118ba1da61473ab33cd8fe889d25ed088ca917df0e0830f6c76db","abstract_canon_sha256":"309235101b408e98c1db1e107f049c40606495752130aaa5e0c4418467627575"},"schema_version":"1.0"},"canonical_sha256":"f408a0e5e80a4bebe62223c7511f5c828a13bf3f0a8290d297422df57410be0f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:53:37.937984Z","signature_b64":"DGIr4vFiM53i7PqleWqxa9udOCml0wYMYSvD/kEyBDQyyfsvc+MXvVMNIn7437GdfVomKXPg+6+XqeZtnN03AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f408a0e5e80a4bebe62223c7511f5c828a13bf3f0a8290d297422df57410be0f","last_reissued_at":"2026-07-05T06:53:37.937573Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:53:37.937573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.12966","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T06:53:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wmW1XtU4xaazDg5b5b3/CMD2c2Ad+TCwTeropPJ3ikkDXIsYO9FHDJiIwAUy6DxF1DZWEKRnfI2aI4YGbtZcAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T03:52:27.030794Z"},"content_sha256":"bd83fed5eda9c481105d122828cec590d8808fe7c807a4a64981410d5e5d9f3d","schema_version":"1.0","event_id":"sha256:bd83fed5eda9c481105d122828cec590d8808fe7c807a4a64981410d5e5d9f3d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:6QEKBZPIBJF6XZRCEPDVCH24QK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchical Integration Diffusion Model for Realistic Image Deblurring","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bin Xia, Ding Liu, Jinjin Gu, Linghe Kong, Xin Yuan, Yulun Zhang, Zheng Chen","submitted_at":"2023-05-22T12:18:20Z","abstract_excerpt":"Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusion model requires a large number of inference iterations to recover the clean image from pure Gaussian noise, which consumes massive computational resources. Moreover, the distribution synthesized by the diffusion model is often misaligned with the target results, leading to restrictions in distortion-based metrics. To address the above issues, we propose the Hierarchical Integration Diffusion Model (HI-Diff), for rea"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12966","kind":"arxiv","version":4},"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/2305.12966/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":"2026-07-05T06:53:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wj8ZYRDEbdMg+XT67tAKxu2PtnX7FAZA8O0rzbp9D3posdxH2UkVVVAlCv2kSX7ykudeidEmOsYvpdvroQeXBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-09T03:52:27.031162Z"},"content_sha256":"087cd426a692241199df09a6eac2f0305a59147700ec09cad6c0890e6cf04649","schema_version":"1.0","event_id":"sha256:087cd426a692241199df09a6eac2f0305a59147700ec09cad6c0890e6cf04649"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6QEKBZPIBJF6XZRCEPDVCH24QK/bundle.json","state_url":"https://pith.science/pith/6QEKBZPIBJF6XZRCEPDVCH24QK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6QEKBZPIBJF6XZRCEPDVCH24QK/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":"2026-07-09T03:52:27Z","links":{"resolver":"https://pith.science/pith/6QEKBZPIBJF6XZRCEPDVCH24QK","bundle":"https://pith.science/pith/6QEKBZPIBJF6XZRCEPDVCH24QK/bundle.json","state":"https://pith.science/pith/6QEKBZPIBJF6XZRCEPDVCH24QK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6QEKBZPIBJF6XZRCEPDVCH24QK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:6QEKBZPIBJF6XZRCEPDVCH24QK","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":"309235101b408e98c1db1e107f049c40606495752130aaa5e0c4418467627575","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-22T12:18:20Z","title_canon_sha256":"2b1e518fe7b118ba1da61473ab33cd8fe889d25ed088ca917df0e0830f6c76db"},"schema_version":"1.0","source":{"id":"2305.12966","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.12966","created_at":"2026-07-05T06:53:37Z"},{"alias_kind":"arxiv_version","alias_value":"2305.12966v4","created_at":"2026-07-05T06:53:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.12966","created_at":"2026-07-05T06:53:37Z"},{"alias_kind":"pith_short_12","alias_value":"6QEKBZPIBJF6","created_at":"2026-07-05T06:53:37Z"},{"alias_kind":"pith_short_16","alias_value":"6QEKBZPIBJF6XZRC","created_at":"2026-07-05T06:53:37Z"},{"alias_kind":"pith_short_8","alias_value":"6QEKBZPI","created_at":"2026-07-05T06:53:37Z"}],"graph_snapshots":[{"event_id":"sha256:087cd426a692241199df09a6eac2f0305a59147700ec09cad6c0890e6cf04649","target":"graph","created_at":"2026-07-05T06:53:37Z","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/2305.12966/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Diffusion models (DMs) have recently been introduced in image deblurring and exhibited promising performance, particularly in terms of details reconstruction. However, the diffusion model requires a large number of inference iterations to recover the clean image from pure Gaussian noise, which consumes massive computational resources. Moreover, the distribution synthesized by the diffusion model is often misaligned with the target results, leading to restrictions in distortion-based metrics. To address the above issues, we propose the Hierarchical Integration Diffusion Model (HI-Diff), for rea","authors_text":"Bin Xia, Ding Liu, Jinjin Gu, Linghe Kong, Xin Yuan, Yulun Zhang, Zheng Chen","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-22T12:18:20Z","title":"Hierarchical Integration Diffusion Model for Realistic Image Deblurring"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.12966","kind":"arxiv","version":4},"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:bd83fed5eda9c481105d122828cec590d8808fe7c807a4a64981410d5e5d9f3d","target":"record","created_at":"2026-07-05T06:53:37Z","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":"309235101b408e98c1db1e107f049c40606495752130aaa5e0c4418467627575","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-05-22T12:18:20Z","title_canon_sha256":"2b1e518fe7b118ba1da61473ab33cd8fe889d25ed088ca917df0e0830f6c76db"},"schema_version":"1.0","source":{"id":"2305.12966","kind":"arxiv","version":4}},"canonical_sha256":"f408a0e5e80a4bebe62223c7511f5c828a13bf3f0a8290d297422df57410be0f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f408a0e5e80a4bebe62223c7511f5c828a13bf3f0a8290d297422df57410be0f","first_computed_at":"2026-07-05T06:53:37.937573Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:53:37.937573Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"DGIr4vFiM53i7PqleWqxa9udOCml0wYMYSvD/kEyBDQyyfsvc+MXvVMNIn7437GdfVomKXPg+6+XqeZtnN03AA==","signature_status":"signed_v1","signed_at":"2026-07-05T06:53:37.937984Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.12966","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd83fed5eda9c481105d122828cec590d8808fe7c807a4a64981410d5e5d9f3d","sha256:087cd426a692241199df09a6eac2f0305a59147700ec09cad6c0890e6cf04649"],"state_sha256":"65fd86a875d241d6a1fff7056426f3d754dcb71662fa0c5b6ef9852ab838c949"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ixujgQGj9upF7kYe0cRhC9dWGwvisJdbVnprPSaPVzvdhmzH4uIS5sNX9eK8M0KMDka4S/GYjgpFKkbKL3QyCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-09T03:52:27.033102Z","bundle_sha256":"e8dc2ab92554bd9a319704e0fb4b518edc4244ce148535dfebe4f7e639bfd520"}}