{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:EWT2TWC2HVEIWPELANMZDHL4NT","short_pith_number":"pith:EWT2TWC2","schema_version":"1.0","canonical_sha256":"25a7a9d85a3d488b3c8b0359919d7c6cc8d426cd2a950d56aeedb26d9dbf48ee","source":{"kind":"arxiv","id":"1605.07003","version":2},"attestation_state":"computed","paper":{"title":"Image Restoration with Locally Selected Class-Adapted Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Afonso M. Teodoro, Jos\\'e M. Bioucas-Dias, M\\'ario A. T. Figueiredo","submitted_at":"2016-05-23T13:00:38Z","abstract_excerpt":"State-of-the-art algorithms for imaging inverse problems (namely deblurring and reconstruction) are typically iterative, involving a denoising operation as one of its steps. Using a state-of-the-art denoising method in this context is not trivial, and is the focus of current work. Recently, we have proposed to use a class-adapted denoiser (patch-based using Gaussian mixture models) in a so-called plug-and-play scheme, wherein a state-of-the-art denoiser is plugged into an iterative algorithm, leading to results that outperform the best general-purpose algorithms, when applied to an image of a "},"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":"1605.07003","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-05-23T13:00:38Z","cross_cats_sorted":[],"title_canon_sha256":"277ce24a05c149bf789e3f7a2a8cd678f330f1fa4d426b47330be3d6e2dbbdf6","abstract_canon_sha256":"834bbf595823e71062b3183672cf2fd68ca99b4c2b2941715771735289657267"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:10:02.903512Z","signature_b64":"HIeYney0oeqWpDX8wpWvpkfUb3LJAs7+aaYgjeVP+UilQwQE4saftAc5cI2svjxN8lr1WR8hwWW55Ug0nR7+DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25a7a9d85a3d488b3c8b0359919d7c6cc8d426cd2a950d56aeedb26d9dbf48ee","last_reissued_at":"2026-05-18T01:10:02.902974Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:10:02.902974Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Image Restoration with Locally Selected Class-Adapted Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Afonso M. Teodoro, Jos\\'e M. Bioucas-Dias, M\\'ario A. T. Figueiredo","submitted_at":"2016-05-23T13:00:38Z","abstract_excerpt":"State-of-the-art algorithms for imaging inverse problems (namely deblurring and reconstruction) are typically iterative, involving a denoising operation as one of its steps. Using a state-of-the-art denoising method in this context is not trivial, and is the focus of current work. Recently, we have proposed to use a class-adapted denoiser (patch-based using Gaussian mixture models) in a so-called plug-and-play scheme, wherein a state-of-the-art denoiser is plugged into an iterative algorithm, leading to results that outperform the best general-purpose algorithms, when applied to an image of a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1605.07003","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":""},"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":"1605.07003","created_at":"2026-05-18T01:10:02.903062+00:00"},{"alias_kind":"arxiv_version","alias_value":"1605.07003v2","created_at":"2026-05-18T01:10:02.903062+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1605.07003","created_at":"2026-05-18T01:10:02.903062+00:00"},{"alias_kind":"pith_short_12","alias_value":"EWT2TWC2HVEI","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_16","alias_value":"EWT2TWC2HVEIWPEL","created_at":"2026-05-18T12:30:15.759754+00:00"},{"alias_kind":"pith_short_8","alias_value":"EWT2TWC2","created_at":"2026-05-18T12:30:15.759754+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EWT2TWC2HVEIWPELANMZDHL4NT","json":"https://pith.science/pith/EWT2TWC2HVEIWPELANMZDHL4NT.json","graph_json":"https://pith.science/api/pith-number/EWT2TWC2HVEIWPELANMZDHL4NT/graph.json","events_json":"https://pith.science/api/pith-number/EWT2TWC2HVEIWPELANMZDHL4NT/events.json","paper":"https://pith.science/paper/EWT2TWC2"},"agent_actions":{"view_html":"https://pith.science/pith/EWT2TWC2HVEIWPELANMZDHL4NT","download_json":"https://pith.science/pith/EWT2TWC2HVEIWPELANMZDHL4NT.json","view_paper":"https://pith.science/paper/EWT2TWC2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1605.07003&json=true","fetch_graph":"https://pith.science/api/pith-number/EWT2TWC2HVEIWPELANMZDHL4NT/graph.json","fetch_events":"https://pith.science/api/pith-number/EWT2TWC2HVEIWPELANMZDHL4NT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EWT2TWC2HVEIWPELANMZDHL4NT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EWT2TWC2HVEIWPELANMZDHL4NT/action/storage_attestation","attest_author":"https://pith.science/pith/EWT2TWC2HVEIWPELANMZDHL4NT/action/author_attestation","sign_citation":"https://pith.science/pith/EWT2TWC2HVEIWPELANMZDHL4NT/action/citation_signature","submit_replication":"https://pith.science/pith/EWT2TWC2HVEIWPELANMZDHL4NT/action/replication_record"}},"created_at":"2026-05-18T01:10:02.903062+00:00","updated_at":"2026-05-18T01:10:02.903062+00:00"}