{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:PICJTA4XIWFO6CCTBABNJ6XFP7","short_pith_number":"pith:PICJTA4X","schema_version":"1.0","canonical_sha256":"7a04998397458aef08530802d4fae57fcf3b26668384a2e5fcd4dc17fb3b8173","source":{"kind":"arxiv","id":"2506.02381","version":1},"attestation_state":"computed","paper":{"title":"Unrolling Nonconvex Graph Total Variation for Image Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Fei Chen, Gene Cheung, Ivan Selesnick, Songlin Wei","submitted_at":"2025-06-03T02:34:32Z","abstract_excerpt":"Conventional model-based image denoising optimizations employ convex regularization terms, such as total variation (TV) that convexifies the $\\ell_0$-norm to promote sparse signal representation. Instead, we propose a new non-convex total variation term in a graph setting (NC-GTV), such that when combined with an $\\ell_2$-norm fidelity term for denoising, leads to a convex objective with no extraneous local minima. We define NC-GTV using a new graph variant of the Huber function, interpretable as a Moreau envelope. The crux is the selection of a parameter $a$ characterizing the graph Huber fun"},"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":"2506.02381","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-06-03T02:34:32Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"c2d823b8a7bf5b00cf42bb88bf16a49d1c5b31bce1f489a8671755d7fadd5d34","abstract_canon_sha256":"0c4a3d548f7a9e0f8d8adf8ec5f7f99d8a5da15153cd5641930be13270706d5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:14:37.832536Z","signature_b64":"L5DzogPPNfbhESmg9lDrUM45v/r4U6KbxqlnsY9TwfWnXZGBMNQ6JL/GxzIsMyW4Qzhi+v+MLkUTb36tIUaTDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a04998397458aef08530802d4fae57fcf3b26668384a2e5fcd4dc17fb3b8173","last_reissued_at":"2026-07-05T11:14:37.832082Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:14:37.832082Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Unrolling Nonconvex Graph Total Variation for Image Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Fei Chen, Gene Cheung, Ivan Selesnick, Songlin Wei","submitted_at":"2025-06-03T02:34:32Z","abstract_excerpt":"Conventional model-based image denoising optimizations employ convex regularization terms, such as total variation (TV) that convexifies the $\\ell_0$-norm to promote sparse signal representation. Instead, we propose a new non-convex total variation term in a graph setting (NC-GTV), such that when combined with an $\\ell_2$-norm fidelity term for denoising, leads to a convex objective with no extraneous local minima. We define NC-GTV using a new graph variant of the Huber function, interpretable as a Moreau envelope. The crux is the selection of a parameter $a$ characterizing the graph Huber fun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.02381","kind":"arxiv","version":1},"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/2506.02381/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":"2506.02381","created_at":"2026-07-05T11:14:37.832143+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.02381v1","created_at":"2026-07-05T11:14:37.832143+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.02381","created_at":"2026-07-05T11:14:37.832143+00:00"},{"alias_kind":"pith_short_12","alias_value":"PICJTA4XIWFO","created_at":"2026-07-05T11:14:37.832143+00:00"},{"alias_kind":"pith_short_16","alias_value":"PICJTA4XIWFO6CCT","created_at":"2026-07-05T11:14:37.832143+00:00"},{"alias_kind":"pith_short_8","alias_value":"PICJTA4X","created_at":"2026-07-05T11:14:37.832143+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.02381","citing_title":"Unrolling Nonconvex Graph Total Variation for Image Denoising","ref_index":2,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PICJTA4XIWFO6CCTBABNJ6XFP7","json":"https://pith.science/pith/PICJTA4XIWFO6CCTBABNJ6XFP7.json","graph_json":"https://pith.science/api/pith-number/PICJTA4XIWFO6CCTBABNJ6XFP7/graph.json","events_json":"https://pith.science/api/pith-number/PICJTA4XIWFO6CCTBABNJ6XFP7/events.json","paper":"https://pith.science/paper/PICJTA4X"},"agent_actions":{"view_html":"https://pith.science/pith/PICJTA4XIWFO6CCTBABNJ6XFP7","download_json":"https://pith.science/pith/PICJTA4XIWFO6CCTBABNJ6XFP7.json","view_paper":"https://pith.science/paper/PICJTA4X","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.02381&json=true","fetch_graph":"https://pith.science/api/pith-number/PICJTA4XIWFO6CCTBABNJ6XFP7/graph.json","fetch_events":"https://pith.science/api/pith-number/PICJTA4XIWFO6CCTBABNJ6XFP7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PICJTA4XIWFO6CCTBABNJ6XFP7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PICJTA4XIWFO6CCTBABNJ6XFP7/action/storage_attestation","attest_author":"https://pith.science/pith/PICJTA4XIWFO6CCTBABNJ6XFP7/action/author_attestation","sign_citation":"https://pith.science/pith/PICJTA4XIWFO6CCTBABNJ6XFP7/action/citation_signature","submit_replication":"https://pith.science/pith/PICJTA4XIWFO6CCTBABNJ6XFP7/action/replication_record"}},"created_at":"2026-07-05T11:14:37.832143+00:00","updated_at":"2026-07-05T11:14:37.832143+00:00"}