{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:XBARUETED66H6HFZMEPX5TVTFK","short_pith_number":"pith:XBARUETE","schema_version":"1.0","canonical_sha256":"b8411a12641fbc7f1cb9611f7eceb32a96195680259fe6fe3d8feb5b6fb22e9a","source":{"kind":"arxiv","id":"2304.02978","version":2},"attestation_state":"computed","paper":{"title":"Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Chunhui Wang, Guohui Yang, Junde Wu, Rao Fu, Xiaoguang Di, Yanwu Xu, Yong Li, Yue Wang, Yu Zhang","submitted_at":"2023-04-06T10:05:54Z","abstract_excerpt":"Image enhancement is a common technique used to mitigate issues such as severe noise, low brightness, low contrast, and color deviation in low-light images. However, providing an optimal high-light image as a reference for low-light image enhancement tasks is impossible, which makes the learning process more difficult than other image processing tasks. As a result, although several low-light image enhancement methods have been proposed, most of them are either too complex or insufficient in addressing all the issues in low-light images. In this paper, to make the learning easier in low-light i"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2304.02978","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-06T10:05:54Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"df89ed8f6ccbbb932d7e6991c37d6a5974627ad484b696fb6d8beae0472118c7","abstract_canon_sha256":"21d14d621d1d93b6318dfcb67a22680765b40d66b28563385dcf7e45e78771a3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:37:32.329999Z","signature_b64":"VVDwhYIU0GcvRFwJjidE85tNKLK1X+7IBSHrS2KyGntGMly0fYigeJend6xSXOabhNc12/f1+7NjDsMVXhCwCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b8411a12641fbc7f1cb9611f7eceb32a96195680259fe6fe3d8feb5b6fb22e9a","last_reissued_at":"2026-07-05T06:37:32.329457Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:37:32.329457Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simplifying Low-Light Image Enhancement Networks with Relative Loss Functions","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Chunhui Wang, Guohui Yang, Junde Wu, Rao Fu, Xiaoguang Di, Yanwu Xu, Yong Li, Yue Wang, Yu Zhang","submitted_at":"2023-04-06T10:05:54Z","abstract_excerpt":"Image enhancement is a common technique used to mitigate issues such as severe noise, low brightness, low contrast, and color deviation in low-light images. However, providing an optimal high-light image as a reference for low-light image enhancement tasks is impossible, which makes the learning process more difficult than other image processing tasks. As a result, although several low-light image enhancement methods have been proposed, most of them are either too complex or insufficient in addressing all the issues in low-light images. In this paper, to make the learning easier in low-light i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.02978","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/2304.02978/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2304.02978","created_at":"2026-07-05T06:37:32.329534+00:00"},{"alias_kind":"arxiv_version","alias_value":"2304.02978v2","created_at":"2026-07-05T06:37:32.329534+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.02978","created_at":"2026-07-05T06:37:32.329534+00:00"},{"alias_kind":"pith_short_12","alias_value":"XBARUETED66H","created_at":"2026-07-05T06:37:32.329534+00:00"},{"alias_kind":"pith_short_16","alias_value":"XBARUETED66H6HFZ","created_at":"2026-07-05T06:37:32.329534+00:00"},{"alias_kind":"pith_short_8","alias_value":"XBARUETE","created_at":"2026-07-05T06:37:32.329534+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.14663","citing_title":"BrightVAE: Luminosity Enhancement in Underexposed Endoscopic Images","ref_index":27,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/XBARUETED66H6HFZMEPX5TVTFK","json":"https://pith.science/pith/XBARUETED66H6HFZMEPX5TVTFK.json","graph_json":"https://pith.science/api/pith-number/XBARUETED66H6HFZMEPX5TVTFK/graph.json","events_json":"https://pith.science/api/pith-number/XBARUETED66H6HFZMEPX5TVTFK/events.json","paper":"https://pith.science/paper/XBARUETE"},"agent_actions":{"view_html":"https://pith.science/pith/XBARUETED66H6HFZMEPX5TVTFK","download_json":"https://pith.science/pith/XBARUETED66H6HFZMEPX5TVTFK.json","view_paper":"https://pith.science/paper/XBARUETE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2304.02978&json=true","fetch_graph":"https://pith.science/api/pith-number/XBARUETED66H6HFZMEPX5TVTFK/graph.json","fetch_events":"https://pith.science/api/pith-number/XBARUETED66H6HFZMEPX5TVTFK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XBARUETED66H6HFZMEPX5TVTFK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XBARUETED66H6HFZMEPX5TVTFK/action/storage_attestation","attest_author":"https://pith.science/pith/XBARUETED66H6HFZMEPX5TVTFK/action/author_attestation","sign_citation":"https://pith.science/pith/XBARUETED66H6HFZMEPX5TVTFK/action/citation_signature","submit_replication":"https://pith.science/pith/XBARUETED66H6HFZMEPX5TVTFK/action/replication_record"}},"created_at":"2026-07-05T06:37:32.329534+00:00","updated_at":"2026-07-05T06:37:32.329534+00:00"}