{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:2FJBGNYGHW4FHNXVCCAVBI4ISF","short_pith_number":"pith:2FJBGNYG","canonical_record":{"source":{"id":"2504.14664","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-20T16:00:38Z","cross_cats_sorted":[],"title_canon_sha256":"821c8a45337dd0209da8a90af3143cb6ce94f04f0078d8fc49487a4e5249632a","abstract_canon_sha256":"970cdeae5d062f40a2b15758dbd6c0b4f4cbef5bd692354de81f30bea7d5fa2d"},"schema_version":"1.0"},"canonical_sha256":"d1521337063db853b6f5108150a388916ae02cd6dcf65690a3664b1f0f9a71f3","source":{"kind":"arxiv","id":"2504.14664","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.14664","created_at":"2026-07-05T10:51:48Z"},{"alias_kind":"arxiv_version","alias_value":"2504.14664v1","created_at":"2026-07-05T10:51:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14664","created_at":"2026-07-05T10:51:48Z"},{"alias_kind":"pith_short_12","alias_value":"2FJBGNYGHW4F","created_at":"2026-07-05T10:51:48Z"},{"alias_kind":"pith_short_16","alias_value":"2FJBGNYGHW4FHNXV","created_at":"2026-07-05T10:51:48Z"},{"alias_kind":"pith_short_8","alias_value":"2FJBGNYG","created_at":"2026-07-05T10:51:48Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:2FJBGNYGHW4FHNXVCCAVBI4ISF","target":"record","payload":{"canonical_record":{"source":{"id":"2504.14664","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-20T16:00:38Z","cross_cats_sorted":[],"title_canon_sha256":"821c8a45337dd0209da8a90af3143cb6ce94f04f0078d8fc49487a4e5249632a","abstract_canon_sha256":"970cdeae5d062f40a2b15758dbd6c0b4f4cbef5bd692354de81f30bea7d5fa2d"},"schema_version":"1.0"},"canonical_sha256":"d1521337063db853b6f5108150a388916ae02cd6dcf65690a3664b1f0f9a71f3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:51:48.545421Z","signature_b64":"tYGngzMBChQxMYu9FVeDpMMgOoBZbtMZ4aKnN3M9t+6fqMSZfHa8p8l8P/GMY66vNN2f0BVa0s3T/TyD7zneDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1521337063db853b6f5108150a388916ae02cd6dcf65690a3664b1f0f9a71f3","last_reissued_at":"2026-07-05T10:51:48.544857Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:51:48.544857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.14664","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":"2026-07-05T10:51:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ClNn/dgkujCGF3jf22uz8gTSZ9DOmxMZ2d+lh1wtnkNgdtA+AxNd6pXuRDlFmX8L2yEPBDUsdPNUV3zNhJxFAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T02:01:49.010839Z"},"content_sha256":"c568913a68f19fb26a89883203fb399dd49e3a8e7475115c2dfa1d75697ade37","schema_version":"1.0","event_id":"sha256:c568913a68f19fb26a89883203fb399dd49e3a8e7475115c2dfa1d75697ade37"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:2FJBGNYGHW4FHNXVCCAVBI4ISF","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Frequency-domain Learning with Kernel Prior for Blind Image Deblurring","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fei Lei, Jiawei Zhang, Jixiang Sun, Wenxiu Sun, Yujiu Yang","submitted_at":"2025-04-20T16:00:38Z","abstract_excerpt":"While achieving excellent results on various datasets, many deep learning methods for image deblurring suffer from limited generalization capabilities with out-of-domain data. This limitation is likely caused by their dependence on certain domain-specific datasets. To address this challenge, we argue that it is necessary to introduce the kernel prior into deep learning methods, as the kernel prior remains independent of the image context. For effective fusion of kernel prior information, we adopt a rational implementation method inspired by traditional deblurring algorithms that perform deconv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14664","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2504.14664/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":"2026-07-05T10:51:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ay9+kFEm88Y1nvml4q7eEIdl95I7QY/KDqJBD1rMONkFdAvWOwSoDOVvS8clt/K02qhvewjajsCb/3/0bG1/Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-17T02:01:49.011343Z"},"content_sha256":"a367566d086bb4ada52255e28e8d1f7251ad5beae97fa34a8fd2baf57a50c80b","schema_version":"1.0","event_id":"sha256:a367566d086bb4ada52255e28e8d1f7251ad5beae97fa34a8fd2baf57a50c80b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2FJBGNYGHW4FHNXVCCAVBI4ISF/bundle.json","state_url":"https://pith.science/pith/2FJBGNYGHW4FHNXVCCAVBI4ISF/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2FJBGNYGHW4FHNXVCCAVBI4ISF/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":"2026-08-17T02:01:49Z","links":{"resolver":"https://pith.science/pith/2FJBGNYGHW4FHNXVCCAVBI4ISF","bundle":"https://pith.science/pith/2FJBGNYGHW4FHNXVCCAVBI4ISF/bundle.json","state":"https://pith.science/pith/2FJBGNYGHW4FHNXVCCAVBI4ISF/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2FJBGNYGHW4FHNXVCCAVBI4ISF/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:2FJBGNYGHW4FHNXVCCAVBI4ISF","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":"970cdeae5d062f40a2b15758dbd6c0b4f4cbef5bd692354de81f30bea7d5fa2d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-20T16:00:38Z","title_canon_sha256":"821c8a45337dd0209da8a90af3143cb6ce94f04f0078d8fc49487a4e5249632a"},"schema_version":"1.0","source":{"id":"2504.14664","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.14664","created_at":"2026-07-05T10:51:48Z"},{"alias_kind":"arxiv_version","alias_value":"2504.14664v1","created_at":"2026-07-05T10:51:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.14664","created_at":"2026-07-05T10:51:48Z"},{"alias_kind":"pith_short_12","alias_value":"2FJBGNYGHW4F","created_at":"2026-07-05T10:51:48Z"},{"alias_kind":"pith_short_16","alias_value":"2FJBGNYGHW4FHNXV","created_at":"2026-07-05T10:51:48Z"},{"alias_kind":"pith_short_8","alias_value":"2FJBGNYG","created_at":"2026-07-05T10:51:48Z"}],"graph_snapshots":[{"event_id":"sha256:a367566d086bb4ada52255e28e8d1f7251ad5beae97fa34a8fd2baf57a50c80b","target":"graph","created_at":"2026-07-05T10:51:48Z","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/2504.14664/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"While achieving excellent results on various datasets, many deep learning methods for image deblurring suffer from limited generalization capabilities with out-of-domain data. This limitation is likely caused by their dependence on certain domain-specific datasets. To address this challenge, we argue that it is necessary to introduce the kernel prior into deep learning methods, as the kernel prior remains independent of the image context. For effective fusion of kernel prior information, we adopt a rational implementation method inspired by traditional deblurring algorithms that perform deconv","authors_text":"Fei Lei, Jiawei Zhang, Jixiang Sun, Wenxiu Sun, Yujiu Yang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-20T16:00:38Z","title":"Frequency-domain Learning with Kernel Prior for Blind Image Deblurring"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.14664","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:c568913a68f19fb26a89883203fb399dd49e3a8e7475115c2dfa1d75697ade37","target":"record","created_at":"2026-07-05T10:51:48Z","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":"970cdeae5d062f40a2b15758dbd6c0b4f4cbef5bd692354de81f30bea7d5fa2d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-04-20T16:00:38Z","title_canon_sha256":"821c8a45337dd0209da8a90af3143cb6ce94f04f0078d8fc49487a4e5249632a"},"schema_version":"1.0","source":{"id":"2504.14664","kind":"arxiv","version":1}},"canonical_sha256":"d1521337063db853b6f5108150a388916ae02cd6dcf65690a3664b1f0f9a71f3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d1521337063db853b6f5108150a388916ae02cd6dcf65690a3664b1f0f9a71f3","first_computed_at":"2026-07-05T10:51:48.544857Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:51:48.544857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tYGngzMBChQxMYu9FVeDpMMgOoBZbtMZ4aKnN3M9t+6fqMSZfHa8p8l8P/GMY66vNN2f0BVa0s3T/TyD7zneDg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:51:48.545421Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.14664","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c568913a68f19fb26a89883203fb399dd49e3a8e7475115c2dfa1d75697ade37","sha256:a367566d086bb4ada52255e28e8d1f7251ad5beae97fa34a8fd2baf57a50c80b"],"state_sha256":"fc1ba5ab56d5204b4c24e96b692fc512ff20581d847656bc2c09fe97136eb847"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jvLRtkuYY3JyhtFOictYEdDKmou+8AwKl/cPKLV7Tyst1Pu2u8s1DRUeS5qS3wIUj3OfLnm6kBtDc4hhwvKCDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-17T02:01:49.016053Z","bundle_sha256":"97a4b81f56ad5a79701f53b8b5d05c50642431349478a58bde3893e3bb44ba82"}}