{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:HES6WQVDU6JHITIZTREA3ZQMMY","short_pith_number":"pith:HES6WQVD","schema_version":"1.0","canonical_sha256":"3925eb42a3a792744d199c480de60c660031cb14204be0f4a44f46274086d8df","source":{"kind":"arxiv","id":"2411.10798","version":2},"attestation_state":"computed","paper":{"title":"Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Haoze Sun, Jiaming Guo, Long Peng, Renjing Pei, Wenbo Li, Xin Di, Yang Cao, Yang Wang, Yong Li, Zheng-Jun Zha","submitted_at":"2024-11-16T13:29:50Z","abstract_excerpt":"Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods primarily focus on generating details from the LR RGB domain, often leading to a lack of richness or fidelity in fine details. In this paper, we pioneer the use of details hidden in RAW data to complement existing RGB-only methods, yielding superior outputs. We argue that key image processing steps in Image Signal Processing, such as denoising and demosaicing, inherently result in the loss of fine details in LR image"},"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":"2411.10798","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-11-16T13:29:50Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"74f00d727181341093cc281b40259d2e5f24e32a18f97e97bd2c557a2ac19109","abstract_canon_sha256":"edb36d094fc39685c29ca44f3dfbcffe74aeba68a8e788a5b25c90e2cdb3311d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:32.783947Z","signature_b64":"/WHTA+Cei+JXGLoFYPukNYIEHQlysXrSUZSf5M6s70TiiUw3PvjDTdE87R30q8HTfpOnVMc7EgG6NIWQWEOgBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3925eb42a3a792744d199c480de60c660031cb14204be0f4a44f46274086d8df","last_reissued_at":"2026-07-05T09:38:32.783465Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:32.783465Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unveiling Hidden Details: A RAW Data-Enhanced Paradigm for Real-World Super-Resolution","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Haoze Sun, Jiaming Guo, Long Peng, Renjing Pei, Wenbo Li, Xin Di, Yang Cao, Yang Wang, Yong Li, Zheng-Jun Zha","submitted_at":"2024-11-16T13:29:50Z","abstract_excerpt":"Real-world image super-resolution (Real SR) aims to generate high-fidelity, detail-rich high-resolution (HR) images from low-resolution (LR) counterparts. Existing Real SR methods primarily focus on generating details from the LR RGB domain, often leading to a lack of richness or fidelity in fine details. In this paper, we pioneer the use of details hidden in RAW data to complement existing RGB-only methods, yielding superior outputs. We argue that key image processing steps in Image Signal Processing, such as denoising and demosaicing, inherently result in the loss of fine details in LR image"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.10798","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/2411.10798/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":"2411.10798","created_at":"2026-07-05T09:38:32.783523+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.10798v2","created_at":"2026-07-05T09:38:32.783523+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.10798","created_at":"2026-07-05T09:38:32.783523+00:00"},{"alias_kind":"pith_short_12","alias_value":"HES6WQVDU6JH","created_at":"2026-07-05T09:38:32.783523+00:00"},{"alias_kind":"pith_short_16","alias_value":"HES6WQVDU6JHITIZ","created_at":"2026-07-05T09:38:32.783523+00:00"},{"alias_kind":"pith_short_8","alias_value":"HES6WQVD","created_at":"2026-07-05T09:38:32.783523+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.24762","citing_title":"4KLSDB: A Large-Scale Dataset for 4K Image Restoration and Generation","ref_index":28,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22139","citing_title":"EventGait: Towards Robust Gait Recognition with Event Streams","ref_index":63,"is_internal_anchor":false},{"citing_arxiv_id":"2605.22186","citing_title":"Event-Illumination Collaborative Low-light Image Enhancement with a High-resolution Real-world Dataset","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18047","citing_title":"GS-STVSR: Ultra-Efficient Continuous Spatio-Temporal Video Super-Resolution via 2D Gaussian Splatting","ref_index":100,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HES6WQVDU6JHITIZTREA3ZQMMY","json":"https://pith.science/pith/HES6WQVDU6JHITIZTREA3ZQMMY.json","graph_json":"https://pith.science/api/pith-number/HES6WQVDU6JHITIZTREA3ZQMMY/graph.json","events_json":"https://pith.science/api/pith-number/HES6WQVDU6JHITIZTREA3ZQMMY/events.json","paper":"https://pith.science/paper/HES6WQVD"},"agent_actions":{"view_html":"https://pith.science/pith/HES6WQVDU6JHITIZTREA3ZQMMY","download_json":"https://pith.science/pith/HES6WQVDU6JHITIZTREA3ZQMMY.json","view_paper":"https://pith.science/paper/HES6WQVD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.10798&json=true","fetch_graph":"https://pith.science/api/pith-number/HES6WQVDU6JHITIZTREA3ZQMMY/graph.json","fetch_events":"https://pith.science/api/pith-number/HES6WQVDU6JHITIZTREA3ZQMMY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HES6WQVDU6JHITIZTREA3ZQMMY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HES6WQVDU6JHITIZTREA3ZQMMY/action/storage_attestation","attest_author":"https://pith.science/pith/HES6WQVDU6JHITIZTREA3ZQMMY/action/author_attestation","sign_citation":"https://pith.science/pith/HES6WQVDU6JHITIZTREA3ZQMMY/action/citation_signature","submit_replication":"https://pith.science/pith/HES6WQVDU6JHITIZTREA3ZQMMY/action/replication_record"}},"created_at":"2026-07-05T09:38:32.783523+00:00","updated_at":"2026-07-05T09:38:32.783523+00:00"}