{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2011:O5YFNEHICDY3XI5DHSCE5CURQP","short_pith_number":"pith:O5YFNEHI","schema_version":"1.0","canonical_sha256":"77705690e810f1bba3a33c844e8a9183fd22adcacd02319461570a293f3249e8","source":{"kind":"arxiv","id":"1112.6272","version":4},"attestation_state":"computed","paper":{"title":"A Majorize-Minimize subspace approach for l2-l0 image regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Anna Jezierska, Emilie Chouzenoux, Hugues Talbot, Jean-Christophe Pesquet","submitted_at":"2011-12-29T11:07:58Z","abstract_excerpt":"In this work, we consider a class of differentiable criteria for sparse image computing problems, where a nonconvex regularization is applied to an arbitrary linear transform of the target image. As special cases, it includes edge-preserving measures or frame-analysis potentials commonly used in image processing. As shown by our asymptotic results, the l2-l0 penalties we consider may be employed to provide approximate solutions to l0-penalized optimization problems. One of the advantages of the proposed approach is that it allows us to derive an efficient Majorize-Minimize subspace algorithm. "},"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":"1112.6272","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.OC","submitted_at":"2011-12-29T11:07:58Z","cross_cats_sorted":[],"title_canon_sha256":"15e87fbdcec3de8aba73760ed52ee3962e6a30c6cfdc1b02e992993918869df9","abstract_canon_sha256":"6635af8c121761c54d062a3b84991774dd514176a473ef559995f4fedde0d870"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:15:06.727339Z","signature_b64":"IRljzTGhDISPP4cPfP7buvXfiGNZbgy12jHU2hlrPupgHljdKOGJTWRysQ6OGNHz1aS4bzxT7OHZcBK4UpazBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77705690e810f1bba3a33c844e8a9183fd22adcacd02319461570a293f3249e8","last_reissued_at":"2026-05-18T03:15:06.726565Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:15:06.726565Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Majorize-Minimize subspace approach for l2-l0 image regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.OC","authors_text":"Anna Jezierska, Emilie Chouzenoux, Hugues Talbot, Jean-Christophe Pesquet","submitted_at":"2011-12-29T11:07:58Z","abstract_excerpt":"In this work, we consider a class of differentiable criteria for sparse image computing problems, where a nonconvex regularization is applied to an arbitrary linear transform of the target image. As special cases, it includes edge-preserving measures or frame-analysis potentials commonly used in image processing. As shown by our asymptotic results, the l2-l0 penalties we consider may be employed to provide approximate solutions to l0-penalized optimization problems. One of the advantages of the proposed approach is that it allows us to derive an efficient Majorize-Minimize subspace algorithm. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1112.6272","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":""},"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":"1112.6272","created_at":"2026-05-18T03:15:06.726668+00:00"},{"alias_kind":"arxiv_version","alias_value":"1112.6272v4","created_at":"2026-05-18T03:15:06.726668+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1112.6272","created_at":"2026-05-18T03:15:06.726668+00:00"},{"alias_kind":"pith_short_12","alias_value":"O5YFNEHICDY3","created_at":"2026-05-18T12:26:37.096874+00:00"},{"alias_kind":"pith_short_16","alias_value":"O5YFNEHICDY3XI5D","created_at":"2026-05-18T12:26:37.096874+00:00"},{"alias_kind":"pith_short_8","alias_value":"O5YFNEHI","created_at":"2026-05-18T12:26:37.096874+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/O5YFNEHICDY3XI5DHSCE5CURQP","json":"https://pith.science/pith/O5YFNEHICDY3XI5DHSCE5CURQP.json","graph_json":"https://pith.science/api/pith-number/O5YFNEHICDY3XI5DHSCE5CURQP/graph.json","events_json":"https://pith.science/api/pith-number/O5YFNEHICDY3XI5DHSCE5CURQP/events.json","paper":"https://pith.science/paper/O5YFNEHI"},"agent_actions":{"view_html":"https://pith.science/pith/O5YFNEHICDY3XI5DHSCE5CURQP","download_json":"https://pith.science/pith/O5YFNEHICDY3XI5DHSCE5CURQP.json","view_paper":"https://pith.science/paper/O5YFNEHI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1112.6272&json=true","fetch_graph":"https://pith.science/api/pith-number/O5YFNEHICDY3XI5DHSCE5CURQP/graph.json","fetch_events":"https://pith.science/api/pith-number/O5YFNEHICDY3XI5DHSCE5CURQP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O5YFNEHICDY3XI5DHSCE5CURQP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O5YFNEHICDY3XI5DHSCE5CURQP/action/storage_attestation","attest_author":"https://pith.science/pith/O5YFNEHICDY3XI5DHSCE5CURQP/action/author_attestation","sign_citation":"https://pith.science/pith/O5YFNEHICDY3XI5DHSCE5CURQP/action/citation_signature","submit_replication":"https://pith.science/pith/O5YFNEHICDY3XI5DHSCE5CURQP/action/replication_record"}},"created_at":"2026-05-18T03:15:06.726668+00:00","updated_at":"2026-05-18T03:15:06.726668+00:00"}