{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:TMS6NCB2H7MM75QPACAWNSEQHO","short_pith_number":"pith:TMS6NCB2","schema_version":"1.0","canonical_sha256":"9b25e6883a3fd8cff60f008166c8903ba427288ee97a1f2ccfd123fe3c9652d6","source":{"kind":"arxiv","id":"2303.15848","version":1},"attestation_state":"computed","paper":{"title":"4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Xiuyi Jia, Zhuoran Zheng","submitted_at":"2023-03-28T09:39:29Z","abstract_excerpt":"Currently, mobile and IoT devices are in dire need of a series of methods to enhance 4K images with limited resource expenditure. The absence of large-scale 4K benchmark datasets hampers progress in this area, especially for dehazing. The challenges in building ultra-high-definition (UHD) dehazing datasets are the absence of estimation methods for UHD depth maps, high-quality 4K depth estimation datasets, and migration strategies for UHD haze images from synthetic to real domains. To address these problems, we develop a novel synthetic method to simulate 4K hazy images (including nighttime and"},"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":"2303.15848","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-03-28T09:39:29Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e537e962ff92b27a178df8269e171c0f027c2e2e1aa83e2585bd3838b7c1c484","abstract_canon_sha256":"2444b22bc8d715e7da704043a60a25e954026126fe38669f49dbe845c86efada"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:55:28.501719Z","signature_b64":"RMwB4XSJV8dO6VpQ10Q9iJmBV+LMproKgG+zYEMNE1pmEguwXtOmSDxQQbsMXsfWgx36W8+gWijwIpnB+PKQCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9b25e6883a3fd8cff60f008166c8903ba427288ee97a1f2ccfd123fe3c9652d6","last_reissued_at":"2026-07-05T05:55:28.501222Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:55:28.501222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Xiuyi Jia, Zhuoran Zheng","submitted_at":"2023-03-28T09:39:29Z","abstract_excerpt":"Currently, mobile and IoT devices are in dire need of a series of methods to enhance 4K images with limited resource expenditure. The absence of large-scale 4K benchmark datasets hampers progress in this area, especially for dehazing. The challenges in building ultra-high-definition (UHD) dehazing datasets are the absence of estimation methods for UHD depth maps, high-quality 4K depth estimation datasets, and migration strategies for UHD haze images from synthetic to real domains. To address these problems, we develop a novel synthetic method to simulate 4K hazy images (including nighttime and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2303.15848","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/2303.15848/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":"2303.15848","created_at":"2026-07-05T05:55:28.501287+00:00"},{"alias_kind":"arxiv_version","alias_value":"2303.15848v1","created_at":"2026-07-05T05:55:28.501287+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2303.15848","created_at":"2026-07-05T05:55:28.501287+00:00"},{"alias_kind":"pith_short_12","alias_value":"TMS6NCB2H7MM","created_at":"2026-07-05T05:55:28.501287+00:00"},{"alias_kind":"pith_short_16","alias_value":"TMS6NCB2H7MM75QP","created_at":"2026-07-05T05:55:28.501287+00:00"},{"alias_kind":"pith_short_8","alias_value":"TMS6NCB2","created_at":"2026-07-05T05:55:28.501287+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.14010","citing_title":"UHD Image Dehazing via anDehazeFormer with Atmospheric-aware KV Cache","ref_index":41,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TMS6NCB2H7MM75QPACAWNSEQHO","json":"https://pith.science/pith/TMS6NCB2H7MM75QPACAWNSEQHO.json","graph_json":"https://pith.science/api/pith-number/TMS6NCB2H7MM75QPACAWNSEQHO/graph.json","events_json":"https://pith.science/api/pith-number/TMS6NCB2H7MM75QPACAWNSEQHO/events.json","paper":"https://pith.science/paper/TMS6NCB2"},"agent_actions":{"view_html":"https://pith.science/pith/TMS6NCB2H7MM75QPACAWNSEQHO","download_json":"https://pith.science/pith/TMS6NCB2H7MM75QPACAWNSEQHO.json","view_paper":"https://pith.science/paper/TMS6NCB2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2303.15848&json=true","fetch_graph":"https://pith.science/api/pith-number/TMS6NCB2H7MM75QPACAWNSEQHO/graph.json","fetch_events":"https://pith.science/api/pith-number/TMS6NCB2H7MM75QPACAWNSEQHO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TMS6NCB2H7MM75QPACAWNSEQHO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TMS6NCB2H7MM75QPACAWNSEQHO/action/storage_attestation","attest_author":"https://pith.science/pith/TMS6NCB2H7MM75QPACAWNSEQHO/action/author_attestation","sign_citation":"https://pith.science/pith/TMS6NCB2H7MM75QPACAWNSEQHO/action/citation_signature","submit_replication":"https://pith.science/pith/TMS6NCB2H7MM75QPACAWNSEQHO/action/replication_record"}},"created_at":"2026-07-05T05:55:28.501287+00:00","updated_at":"2026-07-05T05:55:28.501287+00:00"}