{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HWPIMKXXEN3JH3QVE5ZHT5BDHQ","short_pith_number":"pith:HWPIMKXX","schema_version":"1.0","canonical_sha256":"3d9e862af7237693ee15277279f4233c256da39b06a68d5650cc3fb0554f2a42","source":{"kind":"arxiv","id":"2312.08606","version":2},"attestation_state":"computed","paper":{"title":"VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongsheng Chen, Hongxia Gao, Liang Chen, Shasha Huang, Sixiang Chen, Tian Ye, Weipeng Yang, Wenbin Zou","submitted_at":"2023-12-14T02:16:27Z","abstract_excerpt":"Night photography often struggles with challenges like low light and blurring, stemming from dark environments and prolonged exposures. Current methods either disregard priors and directly fitting end-to-end networks, leading to inconsistent illumination, or rely on unreliable handcrafted priors to constrain the network, thereby bringing the greater error to the final result. We believe in the strength of data-driven high-quality priors and strive to offer a reliable and consistent prior, circumventing the restrictions of manual priors. In this paper, we propose Clearer Night Image Restoration"},"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":"2312.08606","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-14T02:16:27Z","cross_cats_sorted":[],"title_canon_sha256":"7548884036797418617128ae9cf3a9ecc0280dbbc8bf7cc3e9217c3606c66450","abstract_canon_sha256":"9e2814c3c0b78bb1b866c35223ac61a67c2443eeb2b990221f810751bc2b2739"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:24:58.501815Z","signature_b64":"JAxu3yiCjAVfgz3IbTmvzGf7bABzjSiFMT+VN9Z3nm/2XAxcK8Qi+EkpqXfQw9bsb5DiGZfPf0txBFix1VWrDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d9e862af7237693ee15277279f4233c256da39b06a68d5650cc3fb0554f2a42","last_reissued_at":"2026-07-05T07:24:58.501323Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:24:58.501323Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VQCNIR: Clearer Night Image Restoration with Vector-Quantized Codebook","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hongsheng Chen, Hongxia Gao, Liang Chen, Shasha Huang, Sixiang Chen, Tian Ye, Weipeng Yang, Wenbin Zou","submitted_at":"2023-12-14T02:16:27Z","abstract_excerpt":"Night photography often struggles with challenges like low light and blurring, stemming from dark environments and prolonged exposures. Current methods either disregard priors and directly fitting end-to-end networks, leading to inconsistent illumination, or rely on unreliable handcrafted priors to constrain the network, thereby bringing the greater error to the final result. We believe in the strength of data-driven high-quality priors and strive to offer a reliable and consistent prior, circumventing the restrictions of manual priors. In this paper, we propose Clearer Night Image Restoration"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.08606","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/2312.08606/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":"2312.08606","created_at":"2026-07-05T07:24:58.501381+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.08606v2","created_at":"2026-07-05T07:24:58.501381+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.08606","created_at":"2026-07-05T07:24:58.501381+00:00"},{"alias_kind":"pith_short_12","alias_value":"HWPIMKXXEN3J","created_at":"2026-07-05T07:24:58.501381+00:00"},{"alias_kind":"pith_short_16","alias_value":"HWPIMKXXEN3JH3QV","created_at":"2026-07-05T07:24:58.501381+00:00"},{"alias_kind":"pith_short_8","alias_value":"HWPIMKXX","created_at":"2026-07-05T07:24:58.501381+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.17885","citing_title":"ISALux: Illumination and Segmentation Aware Transformer Employing Mixture of Experts for Low Light Image Enhancement","ref_index":59,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HWPIMKXXEN3JH3QVE5ZHT5BDHQ","json":"https://pith.science/pith/HWPIMKXXEN3JH3QVE5ZHT5BDHQ.json","graph_json":"https://pith.science/api/pith-number/HWPIMKXXEN3JH3QVE5ZHT5BDHQ/graph.json","events_json":"https://pith.science/api/pith-number/HWPIMKXXEN3JH3QVE5ZHT5BDHQ/events.json","paper":"https://pith.science/paper/HWPIMKXX"},"agent_actions":{"view_html":"https://pith.science/pith/HWPIMKXXEN3JH3QVE5ZHT5BDHQ","download_json":"https://pith.science/pith/HWPIMKXXEN3JH3QVE5ZHT5BDHQ.json","view_paper":"https://pith.science/paper/HWPIMKXX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.08606&json=true","fetch_graph":"https://pith.science/api/pith-number/HWPIMKXXEN3JH3QVE5ZHT5BDHQ/graph.json","fetch_events":"https://pith.science/api/pith-number/HWPIMKXXEN3JH3QVE5ZHT5BDHQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HWPIMKXXEN3JH3QVE5ZHT5BDHQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HWPIMKXXEN3JH3QVE5ZHT5BDHQ/action/storage_attestation","attest_author":"https://pith.science/pith/HWPIMKXXEN3JH3QVE5ZHT5BDHQ/action/author_attestation","sign_citation":"https://pith.science/pith/HWPIMKXXEN3JH3QVE5ZHT5BDHQ/action/citation_signature","submit_replication":"https://pith.science/pith/HWPIMKXXEN3JH3QVE5ZHT5BDHQ/action/replication_record"}},"created_at":"2026-07-05T07:24:58.501381+00:00","updated_at":"2026-07-05T07:24:58.501381+00:00"}