{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:QAUVB2HEVWDXJKTRE23UOL2GGF","short_pith_number":"pith:QAUVB2HE","schema_version":"1.0","canonical_sha256":"802950e8e4ad8774aa7126b7472f4631648eee1dcf38127bdb331533a793f814","source":{"kind":"arxiv","id":"2201.13256","version":4},"attestation_state":"computed","paper":{"title":"Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"math.OC","authors_text":"Arthur Leclaire, Nicolas Papadakis, Samuel Hurault","submitted_at":"2022-01-31T14:05:20Z","abstract_excerpt":"Plug-and-Play (PnP) methods solve ill-posed inverse problems through iterative proximal algorithms by replacing a proximal operator by a denoising operation. When applied with deep neural network denoisers, these methods have shown state-of-the-art visual performance for image restoration problems. However, their theoretical convergence analysis is still incomplete. Most of the existing convergence results consider nonexpansive denoisers, which is non-realistic, or limit their analysis to strongly convex data-fidelity terms in the inverse problem to solve. Recently, it was proposed to train th"},"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":"2201.13256","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2022-01-31T14:05:20Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"31ed6b082364bb68814f35cb5559f69eca233943be00af66760402162e55198e","abstract_canon_sha256":"09f815f64df59feba4d04ac799d39cff048cfec65e6b2e0cf0a4419d4504f0b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:33:29.650023Z","signature_b64":"TO05CdiO+WmxFa03656RPjEYsaOlv4xfZm4MNfeXvB/3OaU35wepeoDrzjogYxG8YLpHe3rZuiID7LPiD/EcDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"802950e8e4ad8774aa7126b7472f4631648eee1dcf38127bdb331533a793f814","last_reissued_at":"2026-07-05T04:33:29.649537Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:33:29.649537Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"math.OC","authors_text":"Arthur Leclaire, Nicolas Papadakis, Samuel Hurault","submitted_at":"2022-01-31T14:05:20Z","abstract_excerpt":"Plug-and-Play (PnP) methods solve ill-posed inverse problems through iterative proximal algorithms by replacing a proximal operator by a denoising operation. When applied with deep neural network denoisers, these methods have shown state-of-the-art visual performance for image restoration problems. However, their theoretical convergence analysis is still incomplete. Most of the existing convergence results consider nonexpansive denoisers, which is non-realistic, or limit their analysis to strongly convex data-fidelity terms in the inverse problem to solve. Recently, it was proposed to train th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.13256","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/2201.13256/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":"2201.13256","created_at":"2026-07-05T04:33:29.649597+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.13256v4","created_at":"2026-07-05T04:33:29.649597+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.13256","created_at":"2026-07-05T04:33:29.649597+00:00"},{"alias_kind":"pith_short_12","alias_value":"QAUVB2HEVWDX","created_at":"2026-07-05T04:33:29.649597+00:00"},{"alias_kind":"pith_short_16","alias_value":"QAUVB2HEVWDXJKTR","created_at":"2026-07-05T04:33:29.649597+00:00"},{"alias_kind":"pith_short_8","alias_value":"QAUVB2HE","created_at":"2026-07-05T04:33:29.649597+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24567","citing_title":"Multilevel Stochastic Plug-and-Play for Sparse-View CT Reconstruction","ref_index":29,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QAUVB2HEVWDXJKTRE23UOL2GGF","json":"https://pith.science/pith/QAUVB2HEVWDXJKTRE23UOL2GGF.json","graph_json":"https://pith.science/api/pith-number/QAUVB2HEVWDXJKTRE23UOL2GGF/graph.json","events_json":"https://pith.science/api/pith-number/QAUVB2HEVWDXJKTRE23UOL2GGF/events.json","paper":"https://pith.science/paper/QAUVB2HE"},"agent_actions":{"view_html":"https://pith.science/pith/QAUVB2HEVWDXJKTRE23UOL2GGF","download_json":"https://pith.science/pith/QAUVB2HEVWDXJKTRE23UOL2GGF.json","view_paper":"https://pith.science/paper/QAUVB2HE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.13256&json=true","fetch_graph":"https://pith.science/api/pith-number/QAUVB2HEVWDXJKTRE23UOL2GGF/graph.json","fetch_events":"https://pith.science/api/pith-number/QAUVB2HEVWDXJKTRE23UOL2GGF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QAUVB2HEVWDXJKTRE23UOL2GGF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QAUVB2HEVWDXJKTRE23UOL2GGF/action/storage_attestation","attest_author":"https://pith.science/pith/QAUVB2HEVWDXJKTRE23UOL2GGF/action/author_attestation","sign_citation":"https://pith.science/pith/QAUVB2HEVWDXJKTRE23UOL2GGF/action/citation_signature","submit_replication":"https://pith.science/pith/QAUVB2HEVWDXJKTRE23UOL2GGF/action/replication_record"}},"created_at":"2026-07-05T04:33:29.649597+00:00","updated_at":"2026-07-05T04:33:29.649597+00:00"}