{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GIRIZ66JISUZ7AMR7YBBW7BQQT","short_pith_number":"pith:GIRIZ66J","schema_version":"1.0","canonical_sha256":"32228cfbc944a99f8191fe021b7c3084d6b7853fc3548984d4b24a219bc98342","source":{"kind":"arxiv","id":"2412.20157","version":3},"attestation_state":"computed","paper":{"title":"UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hang Xu, Hongzhi Zhang, Jingbo Lin, Renjing Pei, Wangmeng Zuo, Wenbo Li, Zhilu Zhang","submitted_at":"2024-12-28T14:09:08Z","abstract_excerpt":"Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are limited in leveraging degradation-specific restoration, and the latter suffer from the inevitable error in degradation estimation. Consequently, the performance of existing methods has a large gap compared to specific single-task models. In this work, we make a step forward in this topic, and present our UniRestorer with improved restoration performance. Specifically, we perform hierarchical clustering on degradatio"},"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":"2412.20157","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-12-28T14:09:08Z","cross_cats_sorted":[],"title_canon_sha256":"e2fd3526bbfa8463e7cd559bc0fd128a9eebb7fa6b353cc1bae35f56f1c7295f","abstract_canon_sha256":"6fc8c9dacba4194d671c8f869766a34df70e65b0d4f6a6a58e85bbc041f8253b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:07:18.611767Z","signature_b64":"qoTFy8Xik7PlQSEutffvVW50U5Nh1j/xcA821yg0LHfHN1YFeLxIs94Rf3DOEWD86TGmowdDEoiG88G+WkWjCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"32228cfbc944a99f8191fe021b7c3084d6b7853fc3548984d4b24a219bc98342","last_reissued_at":"2026-07-05T11:07:18.611229Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:07:18.611229Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hang Xu, Hongzhi Zhang, Jingbo Lin, Renjing Pei, Wangmeng Zuo, Wenbo Li, Zhilu Zhang","submitted_at":"2024-12-28T14:09:08Z","abstract_excerpt":"Recently, considerable progress has been made in all-in-one image restoration. Generally, existing methods can be degradation-agnostic or degradation-aware. However, the former are limited in leveraging degradation-specific restoration, and the latter suffer from the inevitable error in degradation estimation. Consequently, the performance of existing methods has a large gap compared to specific single-task models. In this work, we make a step forward in this topic, and present our UniRestorer with improved restoration performance. Specifically, we perform hierarchical clustering on degradatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.20157","kind":"arxiv","version":3},"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/2412.20157/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":"2412.20157","created_at":"2026-07-05T11:07:18.611291+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.20157v3","created_at":"2026-07-05T11:07:18.611291+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.20157","created_at":"2026-07-05T11:07:18.611291+00:00"},{"alias_kind":"pith_short_12","alias_value":"GIRIZ66JISUZ","created_at":"2026-07-05T11:07:18.611291+00:00"},{"alias_kind":"pith_short_16","alias_value":"GIRIZ66JISUZ7AMR","created_at":"2026-07-05T11:07:18.611291+00:00"},{"alias_kind":"pith_short_8","alias_value":"GIRIZ66J","created_at":"2026-07-05T11:07:18.611291+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07287","citing_title":"SplatWeaver: Learning to Allocate Gaussian Primitives for Generalizable Novel View Synthesis","ref_index":71,"is_internal_anchor":false},{"citing_arxiv_id":"2604.09313","citing_title":"Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2605.07287","citing_title":"SplatWeaver: Learning to Allocate Gaussian Primitives for Generalizable Novel View Synthesis","ref_index":71,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/GIRIZ66JISUZ7AMR7YBBW7BQQT","json":"https://pith.science/pith/GIRIZ66JISUZ7AMR7YBBW7BQQT.json","graph_json":"https://pith.science/api/pith-number/GIRIZ66JISUZ7AMR7YBBW7BQQT/graph.json","events_json":"https://pith.science/api/pith-number/GIRIZ66JISUZ7AMR7YBBW7BQQT/events.json","paper":"https://pith.science/paper/GIRIZ66J"},"agent_actions":{"view_html":"https://pith.science/pith/GIRIZ66JISUZ7AMR7YBBW7BQQT","download_json":"https://pith.science/pith/GIRIZ66JISUZ7AMR7YBBW7BQQT.json","view_paper":"https://pith.science/paper/GIRIZ66J","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.20157&json=true","fetch_graph":"https://pith.science/api/pith-number/GIRIZ66JISUZ7AMR7YBBW7BQQT/graph.json","fetch_events":"https://pith.science/api/pith-number/GIRIZ66JISUZ7AMR7YBBW7BQQT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GIRIZ66JISUZ7AMR7YBBW7BQQT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GIRIZ66JISUZ7AMR7YBBW7BQQT/action/storage_attestation","attest_author":"https://pith.science/pith/GIRIZ66JISUZ7AMR7YBBW7BQQT/action/author_attestation","sign_citation":"https://pith.science/pith/GIRIZ66JISUZ7AMR7YBBW7BQQT/action/citation_signature","submit_replication":"https://pith.science/pith/GIRIZ66JISUZ7AMR7YBBW7BQQT/action/replication_record"}},"created_at":"2026-07-05T11:07:18.611291+00:00","updated_at":"2026-07-05T11:07:18.611291+00:00"}