{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:SRKM3U6QED7UL6VGWRVJNSB5ZR","short_pith_number":"pith:SRKM3U6Q","canonical_record":{"source":{"id":"2607.24002","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-27T04:55:23Z","cross_cats_sorted":[],"title_canon_sha256":"b56ccda39df5e137db7a63c148483d449decc3e26046351d46075db5c00b2efc","abstract_canon_sha256":"cdab231d85b7b53705b2843d8d0de94d980b12bba6d6047ba6dbcb069f21d22d"},"schema_version":"1.0"},"canonical_sha256":"9454cdd3d020ff45faa6b46a96c83dcc509006d61de2cd63c850b3a00c4f0115","source":{"kind":"arxiv","id":"2607.24002","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.24002","created_at":"2026-07-28T01:23:40Z"},{"alias_kind":"arxiv_version","alias_value":"2607.24002v1","created_at":"2026-07-28T01:23:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.24002","created_at":"2026-07-28T01:23:40Z"},{"alias_kind":"pith_short_12","alias_value":"SRKM3U6QED7U","created_at":"2026-07-28T01:23:40Z"},{"alias_kind":"pith_short_16","alias_value":"SRKM3U6QED7UL6VG","created_at":"2026-07-28T01:23:40Z"},{"alias_kind":"pith_short_8","alias_value":"SRKM3U6Q","created_at":"2026-07-28T01:23:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:SRKM3U6QED7UL6VGWRVJNSB5ZR","target":"record","payload":{"canonical_record":{"source":{"id":"2607.24002","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-27T04:55:23Z","cross_cats_sorted":[],"title_canon_sha256":"b56ccda39df5e137db7a63c148483d449decc3e26046351d46075db5c00b2efc","abstract_canon_sha256":"cdab231d85b7b53705b2843d8d0de94d980b12bba6d6047ba6dbcb069f21d22d"},"schema_version":"1.0"},"canonical_sha256":"9454cdd3d020ff45faa6b46a96c83dcc509006d61de2cd63c850b3a00c4f0115","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T01:23:40.585160Z","signature_b64":"Wd+iS6Ai1l0gwzdSPWtFROxS85l4mRF6DGd86qiDPQuxUBpmkSHlVGTwqJmhmZ9NbJ+MzDSj2UuSLQ7V04JYAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9454cdd3d020ff45faa6b46a96c83dcc509006d61de2cd63c850b3a00c4f0115","last_reissued_at":"2026-07-28T01:23:40.584258Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T01:23:40.584258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.24002","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":"2026-07-28T01:23:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CzUfWDvSxSr/cqKgOTCibW9aTP5XiwZ74d2IHhgSRIRDkXDc53qHD7n4CTbduhVgWvnssO7uf+ByITwm3L3FAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:57:10.510235Z"},"content_sha256":"e0ae7739c4a02507bfa1a61d2d1005c2af2a6d33e3c9670cf05cf7571fec2780","schema_version":"1.0","event_id":"sha256:e0ae7739c4a02507bfa1a61d2d1005c2af2a6d33e3c9670cf05cf7571fec2780"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:SRKM3U6QED7UL6VGWRVJNSB5ZR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Low-light Image Enhancement via Multi-scale Attention combined with Fourier Transform","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jian Long, Wenbin Du, Zhu Cao","submitted_at":"2026-07-27T04:55:23Z","abstract_excerpt":"Low-light image enhancement (LLIE) aims to improve image quality and clarity in diverse and demanding low-illumination environments. However, existing deep learning-based LLIE methods struggle to accurately capture real-world illumination and restore texture details, largely because their algorithmic strengths remain underutilized. To address these issues, we present a supervised frequency domain deep learning network for LLIE, named multi-scale attention combined with the Fourier transform (MSFT) which adopts a U-shaped, one-stage architecture that infuses guidance from low-light images into "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.24002","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/2607.24002/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":"2026-07-28T01:23:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dOCflrrDFgcPEh8pRq0qKU5vCrfIjJJC1UzNIwxresLp7f6RSYl/cGXdl98uYoUGJDi7sOYpQb4toEYOe//lBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:57:10.510641Z"},"content_sha256":"5e1494b9b039052192a0d599e95ea90d5f5676635f3ea9f6c723704d97717d29","schema_version":"1.0","event_id":"sha256:5e1494b9b039052192a0d599e95ea90d5f5676635f3ea9f6c723704d97717d29"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:SRKM3U6QED7UL6VGWRVJNSB5ZR","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/CVPR.2018.00347) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"C.Chen, Q.Chen, J.Xu, V.Koltun, Learningtoseeinthedark, in: 2018IEEE/CVFConference on Computer Vision and Pattern Recognition, 2018, pp. 3291–3300.doi:10.1109/CVPR.2018. 00347","arxiv_id":"2607.24002","detector":"doi_compliance","evidence":{"ref_index":69,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"10.1109/cvpr.2018","reconstructed_doi":"10.1109/CVPR.2018.00347"},"severity":"advisory","ref_index":69,"audited_at":"2026-07-31T23:32:00.781593Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/CVPR.2018.00347","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"9054b3154de2f3b71a00788365898e954718a1166a5acb6010cb148752f6f889","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":14604,"payload_sha256":"0799b3a35a1cc8121026d2299b4aec7d96f52b9100ba7da983677a30a3dfeab9","signature_b64":"AHzHhNSspDy6SDh961AO145/wC88FuiAVGRxkE4wlzDB+g9yqJphUMyggkMZyVLt2bNTXrvIJ/il+SwAUecpDw==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T23:36:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9kcypTd5wp8rMdvGbn1xQYl5JyX/+QW5OwUH0iT92rjhnmOmohNNivTfJQSLOL4EsbQCKNvzkBkEZ/HeZdDSBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:57:10.513843Z"},"content_sha256":"80591ff1a32e5bbffbb9b1de3be1cf6430cd0ed33744a9ca8012559bbcb473f7","schema_version":"1.0","event_id":"sha256:80591ff1a32e5bbffbb9b1de3be1cf6430cd0ed33744a9ca8012559bbcb473f7"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:SRKM3U6QED7UL6VGWRVJNSB5ZR","target":"integrity","payload":{"note":"DOI is split by whitespace or line breaks in the printed bibliography. Reconstructed DOI 10.1016/j.ijleo.2019.02.054 resolves to 'Image contrast and color enhancement using adaptive gamma correction and histogram equalization'. A reader following the printed text alone cannot reach it.","snippet":"M. Veluchamy, B. Subramani, Image contrast and color enhancement using adaptive gamma correction and histogram equalization, Optik (2019) 329–337doi:10.1016/j.ijleo.2019.02. 054. URLhttp://dx.doi.org/10.1016/j.ijleo.2019.02.054","arxiv_id":"2607.24002","detector":"doi_compliance","evidence":{"ref_index":35,"verdict_class":"incontrovertible","resolved_title":"Image contrast and color enhancement using adaptive gamma correction and histogram equalization","printed_excerpt":"10.1016/j.ijleo.2019.02","reconstructed_doi":"10.1016/j.ijleo.2019.02.054"},"severity":"advisory","ref_index":35,"audited_at":"2026-07-31T23:32:00.781593Z","event_type":"pith.integrity.v1","detected_doi":"10.1016/j.ijleo.2019.02.054","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"6ec50484e4360a09d99bfbc39a3e44d8b90ff413a7810eb961cbd20b270c6c1a","paper_version":1,"verdict_class":"incontrovertible","resolved_title":"Image contrast and color enhancement using adaptive gamma correction and histogram equalization","detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":14603,"payload_sha256":"5d3cc1e6faeee7d8089a1f58e264106fdee516ad3773fa4a2d5b6cd57372b8fe","signature_b64":"/0TGLs2y0BMAYq7kOMI20NzmSuj1oMFB35xyBZhXFczEC3APC69OobA3HOF1Y1wSJ3DM2OWYVKPUVlNZUKZBAg==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T23:36:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3c93uGaq/ifbYZ890MojPOm2hAZziMnD9U5YZlZG1HzjBP/YB/oEeREIb+Zuf4wbCF2QV6YfqL2QLc4axX1VDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-05T14:57:10.514212Z"},"content_sha256":"449146bea54ef24e78b673b7747a977c5c93a68146c5a1af49a079f02ccab501","schema_version":"1.0","event_id":"sha256:449146bea54ef24e78b673b7747a977c5c93a68146c5a1af49a079f02ccab501"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SRKM3U6QED7UL6VGWRVJNSB5ZR/bundle.json","state_url":"https://pith.science/pith/SRKM3U6QED7UL6VGWRVJNSB5ZR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SRKM3U6QED7UL6VGWRVJNSB5ZR/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":"2026-08-05T14:57:10Z","links":{"resolver":"https://pith.science/pith/SRKM3U6QED7UL6VGWRVJNSB5ZR","bundle":"https://pith.science/pith/SRKM3U6QED7UL6VGWRVJNSB5ZR/bundle.json","state":"https://pith.science/pith/SRKM3U6QED7UL6VGWRVJNSB5ZR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SRKM3U6QED7UL6VGWRVJNSB5ZR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:SRKM3U6QED7UL6VGWRVJNSB5ZR","merge_version":"pith-open-graph-merge-v1","event_count":4,"valid_event_count":4,"invalid_event_count":0,"equivocation_count":1,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"cdab231d85b7b53705b2843d8d0de94d980b12bba6d6047ba6dbcb069f21d22d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-27T04:55:23Z","title_canon_sha256":"b56ccda39df5e137db7a63c148483d449decc3e26046351d46075db5c00b2efc"},"schema_version":"1.0","source":{"id":"2607.24002","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.24002","created_at":"2026-07-28T01:23:40Z"},{"alias_kind":"arxiv_version","alias_value":"2607.24002v1","created_at":"2026-07-28T01:23:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.24002","created_at":"2026-07-28T01:23:40Z"},{"alias_kind":"pith_short_12","alias_value":"SRKM3U6QED7U","created_at":"2026-07-28T01:23:40Z"},{"alias_kind":"pith_short_16","alias_value":"SRKM3U6QED7UL6VG","created_at":"2026-07-28T01:23:40Z"},{"alias_kind":"pith_short_8","alias_value":"SRKM3U6Q","created_at":"2026-07-28T01:23:40Z"}],"graph_snapshots":[{"event_id":"sha256:5e1494b9b039052192a0d599e95ea90d5f5676635f3ea9f6c723704d97717d29","target":"graph","created_at":"2026-07-28T01:23:40Z","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/2607.24002/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Low-light image enhancement (LLIE) aims to improve image quality and clarity in diverse and demanding low-illumination environments. However, existing deep learning-based LLIE methods struggle to accurately capture real-world illumination and restore texture details, largely because their algorithmic strengths remain underutilized. To address these issues, we present a supervised frequency domain deep learning network for LLIE, named multi-scale attention combined with the Fourier transform (MSFT) which adopts a U-shaped, one-stage architecture that infuses guidance from low-light images into ","authors_text":"Jian Long, Wenbin Du, Zhu Cao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-27T04:55:23Z","title":"Low-light Image Enhancement via Multi-scale Attention combined with Fourier Transform"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.24002","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:e0ae7739c4a02507bfa1a61d2d1005c2af2a6d33e3c9670cf05cf7571fec2780","target":"record","created_at":"2026-07-28T01:23:40Z","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":"cdab231d85b7b53705b2843d8d0de94d980b12bba6d6047ba6dbcb069f21d22d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-07-27T04:55:23Z","title_canon_sha256":"b56ccda39df5e137db7a63c148483d449decc3e26046351d46075db5c00b2efc"},"schema_version":"1.0","source":{"id":"2607.24002","kind":"arxiv","version":1}},"canonical_sha256":"9454cdd3d020ff45faa6b46a96c83dcc509006d61de2cd63c850b3a00c4f0115","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9454cdd3d020ff45faa6b46a96c83dcc509006d61de2cd63c850b3a00c4f0115","first_computed_at":"2026-07-28T01:23:40.584258Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-28T01:23:40.584258Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Wd+iS6Ai1l0gwzdSPWtFROxS85l4mRF6DGd86qiDPQuxUBpmkSHlVGTwqJmhmZ9NbJ+MzDSj2UuSLQ7V04JYAQ==","signature_status":"signed_v1","signed_at":"2026-07-28T01:23:40.585160Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.24002","source_kind":"arxiv","source_version":1}}},"equivocations":[{"signer_id":"pith.science","event_type":"integrity_finding","target":"integrity","event_ids":["sha256:449146bea54ef24e78b673b7747a977c5c93a68146c5a1af49a079f02ccab501","sha256:80591ff1a32e5bbffbb9b1de3be1cf6430cd0ed33744a9ca8012559bbcb473f7"]}],"invalid_events":[],"applied_event_ids":["sha256:e0ae7739c4a02507bfa1a61d2d1005c2af2a6d33e3c9670cf05cf7571fec2780","sha256:5e1494b9b039052192a0d599e95ea90d5f5676635f3ea9f6c723704d97717d29"],"state_sha256":"7e466fc026571f717114793c3e0a39571e3658d42c827d219f5d4fa3d553643e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qoACdC1oalkkIMWNeP8NbxYp7I9H3XKYiIeX8n+Vt/r4mof1wOXup2l31VXN9/FS4IOquqziZqivJIyLfkSMAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-05T14:57:10.516045Z","bundle_sha256":"c47ecd4e7c58baf4873607c78043e9a93e19527d67f86d0bf19102d99120bbfe"}}