{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:DRD3ODUXDEFQWMLH47DDSFDDB3","short_pith_number":"pith:DRD3ODUX","schema_version":"1.0","canonical_sha256":"1c47b70e97190b0b3167e7c63914630ee15c01d8c610c44732413fea6aafc605","source":{"kind":"arxiv","id":"2407.08939","version":1},"attestation_state":"computed","paper":{"title":"LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ao Luo, Hai Jiang, Shuaicheng Liu, Songchen Han, Xiaohong Liu","submitted_at":"2024-07-12T02:54:43Z","abstract_excerpt":"In this paper, we propose a diffusion-based unsupervised framework that incorporates physically explainable Retinex theory with diffusion models for low-light image enhancement, named LightenDiffusion. Specifically, we present a content-transfer decomposition network that performs Retinex decomposition within the latent space instead of image space as in previous approaches, enabling the encoded features of unpaired low-light and normal-light images to be decomposed into content-rich reflectance maps and content-free illumination maps. Subsequently, the reflectance map of the low-light image a"},"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":"2407.08939","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-07-12T02:54:43Z","cross_cats_sorted":[],"title_canon_sha256":"132c00b4c62f39e341f009db558991dd9f05412459ee736c5b3276bc00aa820a","abstract_canon_sha256":"96ce74f464e372ff825ed790e50cf1a380ae057a6263de508b6895f6542e2af0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:42:59.013981Z","signature_b64":"luq2pC49A/ypB8AJ3u3lW/7ImB7TPTyrF32dq92SZgsTOkGf7Oj1BXA6mZBLwb6FfWoj0ohZS1vZbmyr2zTNDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1c47b70e97190b0b3167e7c63914630ee15c01d8c610c44732413fea6aafc605","last_reissued_at":"2026-07-05T08:42:59.013407Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:42:59.013407Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LightenDiffusion: Unsupervised Low-Light Image Enhancement with Latent-Retinex Diffusion Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ao Luo, Hai Jiang, Shuaicheng Liu, Songchen Han, Xiaohong Liu","submitted_at":"2024-07-12T02:54:43Z","abstract_excerpt":"In this paper, we propose a diffusion-based unsupervised framework that incorporates physically explainable Retinex theory with diffusion models for low-light image enhancement, named LightenDiffusion. Specifically, we present a content-transfer decomposition network that performs Retinex decomposition within the latent space instead of image space as in previous approaches, enabling the encoded features of unpaired low-light and normal-light images to be decomposed into content-rich reflectance maps and content-free illumination maps. Subsequently, the reflectance map of the low-light image a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.08939","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/2407.08939/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":"2407.08939","created_at":"2026-07-05T08:42:59.013477+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.08939v1","created_at":"2026-07-05T08:42:59.013477+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.08939","created_at":"2026-07-05T08:42:59.013477+00:00"},{"alias_kind":"pith_short_12","alias_value":"DRD3ODUXDEFQ","created_at":"2026-07-05T08:42:59.013477+00:00"},{"alias_kind":"pith_short_16","alias_value":"DRD3ODUXDEFQWMLH","created_at":"2026-07-05T08:42:59.013477+00:00"},{"alias_kind":"pith_short_8","alias_value":"DRD3ODUX","created_at":"2026-07-05T08:42:59.013477+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2507.04277","citing_title":"Towards Lightest Low-Light Image Enhancement Architecture for Mobile Devices","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DRD3ODUXDEFQWMLH47DDSFDDB3","json":"https://pith.science/pith/DRD3ODUXDEFQWMLH47DDSFDDB3.json","graph_json":"https://pith.science/api/pith-number/DRD3ODUXDEFQWMLH47DDSFDDB3/graph.json","events_json":"https://pith.science/api/pith-number/DRD3ODUXDEFQWMLH47DDSFDDB3/events.json","paper":"https://pith.science/paper/DRD3ODUX"},"agent_actions":{"view_html":"https://pith.science/pith/DRD3ODUXDEFQWMLH47DDSFDDB3","download_json":"https://pith.science/pith/DRD3ODUXDEFQWMLH47DDSFDDB3.json","view_paper":"https://pith.science/paper/DRD3ODUX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.08939&json=true","fetch_graph":"https://pith.science/api/pith-number/DRD3ODUXDEFQWMLH47DDSFDDB3/graph.json","fetch_events":"https://pith.science/api/pith-number/DRD3ODUXDEFQWMLH47DDSFDDB3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DRD3ODUXDEFQWMLH47DDSFDDB3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DRD3ODUXDEFQWMLH47DDSFDDB3/action/storage_attestation","attest_author":"https://pith.science/pith/DRD3ODUXDEFQWMLH47DDSFDDB3/action/author_attestation","sign_citation":"https://pith.science/pith/DRD3ODUXDEFQWMLH47DDSFDDB3/action/citation_signature","submit_replication":"https://pith.science/pith/DRD3ODUXDEFQWMLH47DDSFDDB3/action/replication_record"}},"created_at":"2026-07-05T08:42:59.013477+00:00","updated_at":"2026-07-05T08:42:59.013477+00:00"}