{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:L4WCTNAFH67JTYC2IVT3JJJ7NT","short_pith_number":"pith:L4WCTNAF","schema_version":"1.0","canonical_sha256":"5f2c29b4053fbe99e05a4567b4a53f6cc683bad10f1ab093f0444747b3f6cd0b","source":{"kind":"arxiv","id":"2310.11881","version":4},"attestation_state":"computed","paper":{"title":"A Comparative Study of Image Restoration Networks for General Backbone Network Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Dong, Jiantao Zhou, Xiangyu Chen, Yihao Liu, Yuandong Pu, Yu Qiao, Zheyuan Li","submitted_at":"2023-10-18T11:06:41Z","abstract_excerpt":"Despite the significant progress made by deep models in various image restoration tasks, existing image restoration networks still face challenges in terms of task generality. An intuitive manifestation is that networks which excel in certain tasks often fail to deliver satisfactory results in others. To illustrate this point, we select five representative networks and conduct a comparative study on five classic image restoration tasks. First, we provide a detailed explanation of the characteristics of different image restoration tasks and backbone networks. Following this, we present the benc"},"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":"2310.11881","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-18T11:06:41Z","cross_cats_sorted":[],"title_canon_sha256":"b183f840c55683551401a396312293e8f5dc6fb4bf29004f613cc737aedfe54b","abstract_canon_sha256":"bcdd6dd97339d2cd865fb20a8567b1b2ebe957ba2a7e491c13086081b9a0a138"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:44:10.202695Z","signature_b64":"CiDYfh3M51m7awWFpjAWipNPU6R0mdQIR3nUwB3DxZSW/fDVEq041dTMyUZR/JkWbemTvhuoroFv0fbL8UMkAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f2c29b4053fbe99e05a4567b4a53f6cc683bad10f1ab093f0444747b3f6cd0b","last_reissued_at":"2026-07-05T08:44:10.202200Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:44:10.202200Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Comparative Study of Image Restoration Networks for General Backbone Network Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chao Dong, Jiantao Zhou, Xiangyu Chen, Yihao Liu, Yuandong Pu, Yu Qiao, Zheyuan Li","submitted_at":"2023-10-18T11:06:41Z","abstract_excerpt":"Despite the significant progress made by deep models in various image restoration tasks, existing image restoration networks still face challenges in terms of task generality. An intuitive manifestation is that networks which excel in certain tasks often fail to deliver satisfactory results in others. To illustrate this point, we select five representative networks and conduct a comparative study on five classic image restoration tasks. First, we provide a detailed explanation of the characteristics of different image restoration tasks and backbone networks. Following this, we present the benc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.11881","kind":"arxiv","version":4},"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/2310.11881/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":"2310.11881","created_at":"2026-07-05T08:44:10.202258+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.11881v4","created_at":"2026-07-05T08:44:10.202258+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.11881","created_at":"2026-07-05T08:44:10.202258+00:00"},{"alias_kind":"pith_short_12","alias_value":"L4WCTNAFH67J","created_at":"2026-07-05T08:44:10.202258+00:00"},{"alias_kind":"pith_short_16","alias_value":"L4WCTNAFH67JTYC2","created_at":"2026-07-05T08:44:10.202258+00:00"},{"alias_kind":"pith_short_8","alias_value":"L4WCTNAF","created_at":"2026-07-05T08:44:10.202258+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.23814","citing_title":"Mapping License Plate Recoverability Under Extreme Viewing Angles for Opportunistic Urban Sensing","ref_index":23,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13258","citing_title":"X-Restormer++: 1st Place Solution for the UG2+ CVPR 2026 All-Weather Restoration Challenge","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2605.13258","citing_title":"X-Restormer++: 1st Place Solution for the UG2+ CVPR 2026 All-Weather Restoration Challenge","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23814","citing_title":"Mapping License Plate Recoverability Under Extreme Viewing Angles for Opportunistic Urban Sensing","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L4WCTNAFH67JTYC2IVT3JJJ7NT","json":"https://pith.science/pith/L4WCTNAFH67JTYC2IVT3JJJ7NT.json","graph_json":"https://pith.science/api/pith-number/L4WCTNAFH67JTYC2IVT3JJJ7NT/graph.json","events_json":"https://pith.science/api/pith-number/L4WCTNAFH67JTYC2IVT3JJJ7NT/events.json","paper":"https://pith.science/paper/L4WCTNAF"},"agent_actions":{"view_html":"https://pith.science/pith/L4WCTNAFH67JTYC2IVT3JJJ7NT","download_json":"https://pith.science/pith/L4WCTNAFH67JTYC2IVT3JJJ7NT.json","view_paper":"https://pith.science/paper/L4WCTNAF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.11881&json=true","fetch_graph":"https://pith.science/api/pith-number/L4WCTNAFH67JTYC2IVT3JJJ7NT/graph.json","fetch_events":"https://pith.science/api/pith-number/L4WCTNAFH67JTYC2IVT3JJJ7NT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L4WCTNAFH67JTYC2IVT3JJJ7NT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L4WCTNAFH67JTYC2IVT3JJJ7NT/action/storage_attestation","attest_author":"https://pith.science/pith/L4WCTNAFH67JTYC2IVT3JJJ7NT/action/author_attestation","sign_citation":"https://pith.science/pith/L4WCTNAFH67JTYC2IVT3JJJ7NT/action/citation_signature","submit_replication":"https://pith.science/pith/L4WCTNAFH67JTYC2IVT3JJJ7NT/action/replication_record"}},"created_at":"2026-07-05T08:44:10.202258+00:00","updated_at":"2026-07-05T08:44:10.202258+00:00"}