{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:65G3SNZQ4QUMLPZP7GP522WLS3","short_pith_number":"pith:65G3SNZQ","schema_version":"1.0","canonical_sha256":"f74db93730e428c5bf2ff99fdd6acb96c9724779c5e0de1ea73dbf8f17cddf3f","source":{"kind":"arxiv","id":"2312.15701","version":2},"attestation_state":"computed","paper":{"title":"Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Deyu Meng, Jiahong Fu, Qi Xie, Zongben Xu","submitted_at":"2023-12-25T11:53:06Z","abstract_excerpt":"The deep unfolding approach has attracted significant attention in computer vision tasks, which well connects conventional image processing modeling manners with more recent deep learning techniques. Specifically, by establishing a direct correspondence between algorithm operators at each implementation step and network modules within each layer, one can rationally construct an almost ``white box'' network architecture with high interpretability. In this architecture, only the predefined component of the proximal operator, known as a proximal network, needs manual configuration, enabling the n"},"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":"2312.15701","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2023-12-25T11:53:06Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"74b2a844f37e690cd17e05e5382dc6796c4cc371e2ca26e76b990dd13e47eb48","abstract_canon_sha256":"350a78e10b33ad4fccc183d8da8d62beb6149c01ff8e92dec7f1d5be50e63c03"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:37:51.263542Z","signature_b64":"IMS6wGbA4eOBXmEG4HiCBDsHOPAWQ8Ahj+hK9ZSi/5JycXrljIQZp7tTlPmK2VMTgoYFCifWtFMnGoclEwtbCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f74db93730e428c5bf2ff99fdd6acb96c9724779c5e0de1ea73dbf8f17cddf3f","last_reissued_at":"2026-07-05T09:37:51.263005Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:37:51.263005Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Deyu Meng, Jiahong Fu, Qi Xie, Zongben Xu","submitted_at":"2023-12-25T11:53:06Z","abstract_excerpt":"The deep unfolding approach has attracted significant attention in computer vision tasks, which well connects conventional image processing modeling manners with more recent deep learning techniques. Specifically, by establishing a direct correspondence between algorithm operators at each implementation step and network modules within each layer, one can rationally construct an almost ``white box'' network architecture with high interpretability. In this architecture, only the predefined component of the proximal operator, known as a proximal network, needs manual configuration, enabling the n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.15701","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/2312.15701/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":"2312.15701","created_at":"2026-07-05T09:37:51.263075+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.15701v2","created_at":"2026-07-05T09:37:51.263075+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.15701","created_at":"2026-07-05T09:37:51.263075+00:00"},{"alias_kind":"pith_short_12","alias_value":"65G3SNZQ4QUM","created_at":"2026-07-05T09:37:51.263075+00:00"},{"alias_kind":"pith_short_16","alias_value":"65G3SNZQ4QUMLPZP","created_at":"2026-07-05T09:37:51.263075+00:00"},{"alias_kind":"pith_short_8","alias_value":"65G3SNZQ","created_at":"2026-07-05T09:37:51.263075+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.05160","citing_title":"Rotation Equivariant Arbitrary-scale Image Super-Resolution","ref_index":69,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/65G3SNZQ4QUMLPZP7GP522WLS3","json":"https://pith.science/pith/65G3SNZQ4QUMLPZP7GP522WLS3.json","graph_json":"https://pith.science/api/pith-number/65G3SNZQ4QUMLPZP7GP522WLS3/graph.json","events_json":"https://pith.science/api/pith-number/65G3SNZQ4QUMLPZP7GP522WLS3/events.json","paper":"https://pith.science/paper/65G3SNZQ"},"agent_actions":{"view_html":"https://pith.science/pith/65G3SNZQ4QUMLPZP7GP522WLS3","download_json":"https://pith.science/pith/65G3SNZQ4QUMLPZP7GP522WLS3.json","view_paper":"https://pith.science/paper/65G3SNZQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.15701&json=true","fetch_graph":"https://pith.science/api/pith-number/65G3SNZQ4QUMLPZP7GP522WLS3/graph.json","fetch_events":"https://pith.science/api/pith-number/65G3SNZQ4QUMLPZP7GP522WLS3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/65G3SNZQ4QUMLPZP7GP522WLS3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/65G3SNZQ4QUMLPZP7GP522WLS3/action/storage_attestation","attest_author":"https://pith.science/pith/65G3SNZQ4QUMLPZP7GP522WLS3/action/author_attestation","sign_citation":"https://pith.science/pith/65G3SNZQ4QUMLPZP7GP522WLS3/action/citation_signature","submit_replication":"https://pith.science/pith/65G3SNZQ4QUMLPZP7GP522WLS3/action/replication_record"}},"created_at":"2026-07-05T09:37:51.263075+00:00","updated_at":"2026-07-05T09:37:51.263075+00:00"}