{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:V7FWGV4VQ56ETYZ7L2DOHUKQZH","short_pith_number":"pith:V7FWGV4V","canonical_record":{"source":{"id":"2403.05808","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-09T06:01:25Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"5ed334718f38eef20e01b6ea59aad322f235e56db4c228e9afe7f586591afd31","abstract_canon_sha256":"78d8cb0ce3986229a491d4719e1889fcb2e9d97c921592615a4c45e9a35b20a0"},"schema_version":"1.0"},"canonical_sha256":"afcb635795877c49e33f5e86e3d150c9d51546fb07ba02cd3e6a9d26c280bb3f","source":{"kind":"arxiv","id":"2403.05808","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.05808","created_at":"2026-07-05T08:41:43Z"},{"alias_kind":"arxiv_version","alias_value":"2403.05808v2","created_at":"2026-07-05T08:41:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.05808","created_at":"2026-07-05T08:41:43Z"},{"alias_kind":"pith_short_12","alias_value":"V7FWGV4VQ56E","created_at":"2026-07-05T08:41:43Z"},{"alias_kind":"pith_short_16","alias_value":"V7FWGV4VQ56ETYZ7","created_at":"2026-07-05T08:41:43Z"},{"alias_kind":"pith_short_8","alias_value":"V7FWGV4V","created_at":"2026-07-05T08:41:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:V7FWGV4VQ56ETYZ7L2DOHUKQZH","target":"record","payload":{"canonical_record":{"source":{"id":"2403.05808","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-09T06:01:25Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"5ed334718f38eef20e01b6ea59aad322f235e56db4c228e9afe7f586591afd31","abstract_canon_sha256":"78d8cb0ce3986229a491d4719e1889fcb2e9d97c921592615a4c45e9a35b20a0"},"schema_version":"1.0"},"canonical_sha256":"afcb635795877c49e33f5e86e3d150c9d51546fb07ba02cd3e6a9d26c280bb3f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:41:43.803963Z","signature_b64":"BIuvY3ZEocG+51ah5z3eW8XHHhGyAgkAzO02s8mAMTQGkN++sml+OebegLnmF2yvq9JhXrihNmoJ+A0jTdtGCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"afcb635795877c49e33f5e86e3d150c9d51546fb07ba02cd3e6a9d26c280bb3f","last_reissued_at":"2026-07-05T08:41:43.803494Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:41:43.803494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2403.05808","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":"2026-07-05T08:41:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QIG8Mn7s4THglrprnvHxefD5Mm0p3H5Mg0lYGc9eGgHYVsof0w3kSJryzVg11xiC7CFqSDLr+0RoiFMG+9b7CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T15:58:42.821915Z"},"content_sha256":"b3b5efc761a281f1438e262a64ff9f64169e4526cac3c441bdd3736328e123a6","schema_version":"1.0","event_id":"sha256:b3b5efc761a281f1438e262a64ff9f64169e4526cac3c441bdd3736328e123a6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:V7FWGV4VQ56ETYZ7L2DOHUKQZH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Boyang Wang, Haorang Wang, Jiawen Yu, Junxiong Lin, Qing Zhao, Shaoqi Yan, Wei Song, Wenqiang Zhang, Xinji Mai, Xuan Tong, Yan Wang, Yuxuan Lin, Zeng Tao","submitted_at":"2024-03-09T06:01:25Z","abstract_excerpt":"Pre-trained diffusion models utilized for image generation encapsulate a substantial reservoir of a priori knowledge pertaining to intricate textures. Harnessing the potential of leveraging this a priori knowledge in the context of image super-resolution presents a compelling avenue. Nonetheless, prevailing diffusion-based methodologies presently overlook the constraints imposed by degradation information on the diffusion process. Furthermore, these methods fail to consider the spatial variability inherent in the estimated blur kernel, stemming from factors such as motion jitter and out-of-foc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.05808","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/2403.05808/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":"2026-07-05T08:41:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oD5jt1oTFqsWeORc3cUFZuWAMXecS5Md84aytsUVO4QG9NWtMVRS7KKpZLuJY9/CSBa5S+U0DMTjZFi4RFG6DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T15:58:42.822655Z"},"content_sha256":"c16fbf3a8039a43e268f189ab6b3c67c11dc815a3c998e122a4996a94912dae5","schema_version":"1.0","event_id":"sha256:c16fbf3a8039a43e268f189ab6b3c67c11dc815a3c998e122a4996a94912dae5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V7FWGV4VQ56ETYZ7L2DOHUKQZH/bundle.json","state_url":"https://pith.science/pith/V7FWGV4VQ56ETYZ7L2DOHUKQZH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V7FWGV4VQ56ETYZ7L2DOHUKQZH/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":"2026-08-20T15:58:42Z","links":{"resolver":"https://pith.science/pith/V7FWGV4VQ56ETYZ7L2DOHUKQZH","bundle":"https://pith.science/pith/V7FWGV4VQ56ETYZ7L2DOHUKQZH/bundle.json","state":"https://pith.science/pith/V7FWGV4VQ56ETYZ7L2DOHUKQZH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V7FWGV4VQ56ETYZ7L2DOHUKQZH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:V7FWGV4VQ56ETYZ7L2DOHUKQZH","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":"78d8cb0ce3986229a491d4719e1889fcb2e9d97c921592615a4c45e9a35b20a0","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-09T06:01:25Z","title_canon_sha256":"5ed334718f38eef20e01b6ea59aad322f235e56db4c228e9afe7f586591afd31"},"schema_version":"1.0","source":{"id":"2403.05808","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2403.05808","created_at":"2026-07-05T08:41:43Z"},{"alias_kind":"arxiv_version","alias_value":"2403.05808v2","created_at":"2026-07-05T08:41:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.05808","created_at":"2026-07-05T08:41:43Z"},{"alias_kind":"pith_short_12","alias_value":"V7FWGV4VQ56E","created_at":"2026-07-05T08:41:43Z"},{"alias_kind":"pith_short_16","alias_value":"V7FWGV4VQ56ETYZ7","created_at":"2026-07-05T08:41:43Z"},{"alias_kind":"pith_short_8","alias_value":"V7FWGV4V","created_at":"2026-07-05T08:41:43Z"}],"graph_snapshots":[{"event_id":"sha256:c16fbf3a8039a43e268f189ab6b3c67c11dc815a3c998e122a4996a94912dae5","target":"graph","created_at":"2026-07-05T08:41:43Z","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/2403.05808/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Pre-trained diffusion models utilized for image generation encapsulate a substantial reservoir of a priori knowledge pertaining to intricate textures. Harnessing the potential of leveraging this a priori knowledge in the context of image super-resolution presents a compelling avenue. Nonetheless, prevailing diffusion-based methodologies presently overlook the constraints imposed by degradation information on the diffusion process. Furthermore, these methods fail to consider the spatial variability inherent in the estimated blur kernel, stemming from factors such as motion jitter and out-of-foc","authors_text":"Boyang Wang, Haorang Wang, Jiawen Yu, Junxiong Lin, Qing Zhao, Shaoqi Yan, Wei Song, Wenqiang Zhang, Xinji Mai, Xuan Tong, Yan Wang, Yuxuan Lin, Zeng Tao","cross_cats":["eess.IV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-09T06:01:25Z","title":"Adaptive Multi-modal Fusion of Spatially Variant Kernel Refinement with Diffusion Model for Blind Image Super-Resolution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.05808","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:b3b5efc761a281f1438e262a64ff9f64169e4526cac3c441bdd3736328e123a6","target":"record","created_at":"2026-07-05T08:41:43Z","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":"78d8cb0ce3986229a491d4719e1889fcb2e9d97c921592615a4c45e9a35b20a0","cross_cats_sorted":["eess.IV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-03-09T06:01:25Z","title_canon_sha256":"5ed334718f38eef20e01b6ea59aad322f235e56db4c228e9afe7f586591afd31"},"schema_version":"1.0","source":{"id":"2403.05808","kind":"arxiv","version":2}},"canonical_sha256":"afcb635795877c49e33f5e86e3d150c9d51546fb07ba02cd3e6a9d26c280bb3f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"afcb635795877c49e33f5e86e3d150c9d51546fb07ba02cd3e6a9d26c280bb3f","first_computed_at":"2026-07-05T08:41:43.803494Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:41:43.803494Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"BIuvY3ZEocG+51ah5z3eW8XHHhGyAgkAzO02s8mAMTQGkN++sml+OebegLnmF2yvq9JhXrihNmoJ+A0jTdtGCg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:41:43.803963Z","signed_message":"canonical_sha256_bytes"},"source_id":"2403.05808","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b3b5efc761a281f1438e262a64ff9f64169e4526cac3c441bdd3736328e123a6","sha256:c16fbf3a8039a43e268f189ab6b3c67c11dc815a3c998e122a4996a94912dae5"],"state_sha256":"68dac8dc8a5d04214a7d8ac819918b2dec7c6f888e9d63fc03ff392414e05a20"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JZMWhe6hGg9D0TMRP5nGGy2D6Mu6iKMPb06vA+B5aJ+xIW0C/f7kc02Mz/Fxos5U9bHeM91Wrle7qrb4RGUoCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T15:58:42.827892Z","bundle_sha256":"a79d1c8296bd5301e72b89d57bc6f290c995de383faa09c09dddab4934b40caa"}}