{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:GDXP3GU5XSARBKVUQ6XFM5UIQJ","short_pith_number":"pith:GDXP3GU5","schema_version":"1.0","canonical_sha256":"30eefd9a9dbc8110aab487ae567688827a611b7115153db229b7fe421b32e08f","source":{"kind":"arxiv","id":"2507.01342","version":2},"attestation_state":"computed","paper":{"title":"Learning Camera-Agnostic White-Balance Preferences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luxi Zhao, Mahmoud Afifi, Michael S. Brown","submitted_at":"2025-07-02T04:11:01Z","abstract_excerpt":"The image signal processor (ISP) pipeline in modern cameras consists of several modules that transform raw sensor data into visually pleasing images in a display color space. Among these, the auto white balance (AWB) module is essential for compensating for scene illumination. However, commercial AWB systems often strive to compute aesthetic white-balance preferences rather than accurate neutral color correction. While learning-based methods have improved AWB accuracy, they typically struggle to generalize across different camera sensors -- an issue for smartphones with multiple cameras. Recen"},"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":"2507.01342","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-02T04:11:01Z","cross_cats_sorted":[],"title_canon_sha256":"d02732732b8f1bbfcb992f476c32c658b139c11fa0b9862b644508f3a7686c6c","abstract_canon_sha256":"a9a5826de286dcd25e6a2d92482ddd0faa948e18c0d51520fdb68e852971d13f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:54:12.583213Z","signature_b64":"eeRGYu9Q430i5IfJq9alLhGWoIwk9CoaVmyOzlB99pyYfKNeF5/vl+Whf2JIqzuwb9xkP7KJC5aOc95zODdiAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30eefd9a9dbc8110aab487ae567688827a611b7115153db229b7fe421b32e08f","last_reissued_at":"2026-07-05T11:54:12.582787Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:54:12.582787Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Learning Camera-Agnostic White-Balance Preferences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Luxi Zhao, Mahmoud Afifi, Michael S. Brown","submitted_at":"2025-07-02T04:11:01Z","abstract_excerpt":"The image signal processor (ISP) pipeline in modern cameras consists of several modules that transform raw sensor data into visually pleasing images in a display color space. Among these, the auto white balance (AWB) module is essential for compensating for scene illumination. However, commercial AWB systems often strive to compute aesthetic white-balance preferences rather than accurate neutral color correction. While learning-based methods have improved AWB accuracy, they typically struggle to generalize across different camera sensors -- an issue for smartphones with multiple cameras. Recen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.01342","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/2507.01342/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":"2507.01342","created_at":"2026-07-05T11:54:12.582843+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.01342v2","created_at":"2026-07-05T11:54:12.582843+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.01342","created_at":"2026-07-05T11:54:12.582843+00:00"},{"alias_kind":"pith_short_12","alias_value":"GDXP3GU5XSAR","created_at":"2026-07-05T11:54:12.582843+00:00"},{"alias_kind":"pith_short_16","alias_value":"GDXP3GU5XSARBKVU","created_at":"2026-07-05T11:54:12.582843+00:00"},{"alias_kind":"pith_short_8","alias_value":"GDXP3GU5","created_at":"2026-07-05T11:54:12.582843+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GDXP3GU5XSARBKVUQ6XFM5UIQJ","json":"https://pith.science/pith/GDXP3GU5XSARBKVUQ6XFM5UIQJ.json","graph_json":"https://pith.science/api/pith-number/GDXP3GU5XSARBKVUQ6XFM5UIQJ/graph.json","events_json":"https://pith.science/api/pith-number/GDXP3GU5XSARBKVUQ6XFM5UIQJ/events.json","paper":"https://pith.science/paper/GDXP3GU5"},"agent_actions":{"view_html":"https://pith.science/pith/GDXP3GU5XSARBKVUQ6XFM5UIQJ","download_json":"https://pith.science/pith/GDXP3GU5XSARBKVUQ6XFM5UIQJ.json","view_paper":"https://pith.science/paper/GDXP3GU5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.01342&json=true","fetch_graph":"https://pith.science/api/pith-number/GDXP3GU5XSARBKVUQ6XFM5UIQJ/graph.json","fetch_events":"https://pith.science/api/pith-number/GDXP3GU5XSARBKVUQ6XFM5UIQJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GDXP3GU5XSARBKVUQ6XFM5UIQJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GDXP3GU5XSARBKVUQ6XFM5UIQJ/action/storage_attestation","attest_author":"https://pith.science/pith/GDXP3GU5XSARBKVUQ6XFM5UIQJ/action/author_attestation","sign_citation":"https://pith.science/pith/GDXP3GU5XSARBKVUQ6XFM5UIQJ/action/citation_signature","submit_replication":"https://pith.science/pith/GDXP3GU5XSARBKVUQ6XFM5UIQJ/action/replication_record"}},"created_at":"2026-07-05T11:54:12.582843+00:00","updated_at":"2026-07-05T11:54:12.582843+00:00"}