{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AJTZ4YWYIGFUJNJAFQ5SHI4AI7","short_pith_number":"pith:AJTZ4YWY","schema_version":"1.0","canonical_sha256":"02679e62d8418b44b5202c3b23a38047eaef4e62fdcb948350175ad04339dc42","source":{"kind":"arxiv","id":"2409.03404","version":2},"attestation_state":"computed","paper":{"title":"KAN See In the Dark","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Aoxiang Ning, Chengyun Song, Jinhong He, Minglong Xue","submitted_at":"2024-09-05T10:41:17Z","abstract_excerpt":"Existing low-light image enhancement methods are difficult to fit the complex nonlinear relationship between normal and low-light images due to uneven illumination and noise effects. The recently proposed Kolmogorov-Arnold networks (KANs) feature spline-based convolutional layers and learnable activation functions, which can effectively capture nonlinear dependencies. In this paper, we design a KAN-Block based on KANs and innovatively apply it to low-light image enhancement. This method effectively alleviates the limitations of current methods constrained by linear network structures and lack "},"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":"2409.03404","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-05T10:41:17Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4e4f59135f5a81c39a67aac6715da8ec04693e5811da2ed95d027f7588688ef1","abstract_canon_sha256":"92f506b7dc9ff736c67e1c2e81d82ee0e02d86fdf5c0864b5ec5c5069f229cf2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:27.797501Z","signature_b64":"G8l93lOIMFyz4YvEY2GwMI73uanx6Da1V0wdJD71CyGNdxBTqLvr+tY/5d2FY+slrsYzufLxBuXw+GC/peOVDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"02679e62d8418b44b5202c3b23a38047eaef4e62fdcb948350175ad04339dc42","last_reissued_at":"2026-07-05T10:10:27.797085Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:27.797085Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"KAN See In the Dark","license":"http://creativecommons.org/publicdomain/zero/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Aoxiang Ning, Chengyun Song, Jinhong He, Minglong Xue","submitted_at":"2024-09-05T10:41:17Z","abstract_excerpt":"Existing low-light image enhancement methods are difficult to fit the complex nonlinear relationship between normal and low-light images due to uneven illumination and noise effects. The recently proposed Kolmogorov-Arnold networks (KANs) feature spline-based convolutional layers and learnable activation functions, which can effectively capture nonlinear dependencies. In this paper, we design a KAN-Block based on KANs and innovatively apply it to low-light image enhancement. This method effectively alleviates the limitations of current methods constrained by linear network structures and lack "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.03404","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/2409.03404/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":"2409.03404","created_at":"2026-07-05T10:10:27.797137+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.03404v2","created_at":"2026-07-05T10:10:27.797137+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.03404","created_at":"2026-07-05T10:10:27.797137+00:00"},{"alias_kind":"pith_short_12","alias_value":"AJTZ4YWYIGFU","created_at":"2026-07-05T10:10:27.797137+00:00"},{"alias_kind":"pith_short_16","alias_value":"AJTZ4YWYIGFUJNJA","created_at":"2026-07-05T10:10:27.797137+00:00"},{"alias_kind":"pith_short_8","alias_value":"AJTZ4YWY","created_at":"2026-07-05T10:10:27.797137+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.13961","citing_title":"Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors Guided Diffusion","ref_index":23,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AJTZ4YWYIGFUJNJAFQ5SHI4AI7","json":"https://pith.science/pith/AJTZ4YWYIGFUJNJAFQ5SHI4AI7.json","graph_json":"https://pith.science/api/pith-number/AJTZ4YWYIGFUJNJAFQ5SHI4AI7/graph.json","events_json":"https://pith.science/api/pith-number/AJTZ4YWYIGFUJNJAFQ5SHI4AI7/events.json","paper":"https://pith.science/paper/AJTZ4YWY"},"agent_actions":{"view_html":"https://pith.science/pith/AJTZ4YWYIGFUJNJAFQ5SHI4AI7","download_json":"https://pith.science/pith/AJTZ4YWYIGFUJNJAFQ5SHI4AI7.json","view_paper":"https://pith.science/paper/AJTZ4YWY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.03404&json=true","fetch_graph":"https://pith.science/api/pith-number/AJTZ4YWYIGFUJNJAFQ5SHI4AI7/graph.json","fetch_events":"https://pith.science/api/pith-number/AJTZ4YWYIGFUJNJAFQ5SHI4AI7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AJTZ4YWYIGFUJNJAFQ5SHI4AI7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AJTZ4YWYIGFUJNJAFQ5SHI4AI7/action/storage_attestation","attest_author":"https://pith.science/pith/AJTZ4YWYIGFUJNJAFQ5SHI4AI7/action/author_attestation","sign_citation":"https://pith.science/pith/AJTZ4YWYIGFUJNJAFQ5SHI4AI7/action/citation_signature","submit_replication":"https://pith.science/pith/AJTZ4YWYIGFUJNJAFQ5SHI4AI7/action/replication_record"}},"created_at":"2026-07-05T10:10:27.797137+00:00","updated_at":"2026-07-05T10:10:27.797137+00:00"}