{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KTUWX3TUXDLD6HUYI53HCKTRKB","short_pith_number":"pith:KTUWX3TU","schema_version":"1.0","canonical_sha256":"54e96bee74b8d63f1e984776712a7150422ae029a3a714cc11e5451782eca0e1","source":{"kind":"arxiv","id":"2409.07040","version":5},"attestation_state":"computed","paper":{"title":"Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Dingwen Zhang, Junwei Han, Longfei Han, Peiliang Huang, Xianmin Chen, Xiaoxu Feng","submitted_at":"2024-09-11T06:12:03Z","abstract_excerpt":"Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge. Many deep learning-based methods have been developed to address this issue and have shown promising results in recent years. However, single-stage methods, which attempt to unify the complex mapping across both domains, leading to limited denoising performance. In contrast, existing two-stage approaches typically overlook the characteristic of demosaicing within the Image Signal Processing (ISP) pipeline, leading to color distortions under var"},"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":"2409.07040","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-09-11T06:12:03Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"29edeb2500026a535a8e79ead04f13190b86d97aacb8dcbfb9151fa6d2fcc782","abstract_canon_sha256":"9c9d67a11758c9292b3c29c3b41ae6d8750ffdd796839e8dac03f8d2b5265a40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:37:10.838117Z","signature_b64":"tuZm5xJk8bNxShJJzN75zQXrw/PZuyyQ4lmwOLJpo3OK0YgfyEgr9If6yifSDZwxW3srTScH1K9VzT8GV8JeBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54e96bee74b8d63f1e984776712a7150422ae029a3a714cc11e5451782eca0e1","last_reissued_at":"2026-07-05T11:37:10.837651Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:37:10.837651Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Dingwen Zhang, Junwei Han, Longfei Han, Peiliang Huang, Xianmin Chen, Xiaoxu Feng","submitted_at":"2024-09-11T06:12:03Z","abstract_excerpt":"Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge. Many deep learning-based methods have been developed to address this issue and have shown promising results in recent years. However, single-stage methods, which attempt to unify the complex mapping across both domains, leading to limited denoising performance. In contrast, existing two-stage approaches typically overlook the characteristic of demosaicing within the Image Signal Processing (ISP) pipeline, leading to color distortions under var"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.07040","kind":"arxiv","version":5},"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/2409.07040/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":"2409.07040","created_at":"2026-07-05T11:37:10.837709+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.07040v5","created_at":"2026-07-05T11:37:10.837709+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.07040","created_at":"2026-07-05T11:37:10.837709+00:00"},{"alias_kind":"pith_short_12","alias_value":"KTUWX3TUXDLD","created_at":"2026-07-05T11:37:10.837709+00:00"},{"alias_kind":"pith_short_16","alias_value":"KTUWX3TUXDLD6HUY","created_at":"2026-07-05T11:37:10.837709+00:00"},{"alias_kind":"pith_short_8","alias_value":"KTUWX3TU","created_at":"2026-07-05T11:37:10.837709+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.21132","citing_title":"Learning to See in the Extremely Dark","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KTUWX3TUXDLD6HUYI53HCKTRKB","json":"https://pith.science/pith/KTUWX3TUXDLD6HUYI53HCKTRKB.json","graph_json":"https://pith.science/api/pith-number/KTUWX3TUXDLD6HUYI53HCKTRKB/graph.json","events_json":"https://pith.science/api/pith-number/KTUWX3TUXDLD6HUYI53HCKTRKB/events.json","paper":"https://pith.science/paper/KTUWX3TU"},"agent_actions":{"view_html":"https://pith.science/pith/KTUWX3TUXDLD6HUYI53HCKTRKB","download_json":"https://pith.science/pith/KTUWX3TUXDLD6HUYI53HCKTRKB.json","view_paper":"https://pith.science/paper/KTUWX3TU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.07040&json=true","fetch_graph":"https://pith.science/api/pith-number/KTUWX3TUXDLD6HUYI53HCKTRKB/graph.json","fetch_events":"https://pith.science/api/pith-number/KTUWX3TUXDLD6HUYI53HCKTRKB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KTUWX3TUXDLD6HUYI53HCKTRKB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KTUWX3TUXDLD6HUYI53HCKTRKB/action/storage_attestation","attest_author":"https://pith.science/pith/KTUWX3TUXDLD6HUYI53HCKTRKB/action/author_attestation","sign_citation":"https://pith.science/pith/KTUWX3TUXDLD6HUYI53HCKTRKB/action/citation_signature","submit_replication":"https://pith.science/pith/KTUWX3TUXDLD6HUYI53HCKTRKB/action/replication_record"}},"created_at":"2026-07-05T11:37:10.837709+00:00","updated_at":"2026-07-05T11:37:10.837709+00:00"}