{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:SDJXHNORTAL6T5PCBW6HBLQ52I","short_pith_number":"pith:SDJXHNOR","schema_version":"1.0","canonical_sha256":"90d373b5d19817e9f5e20dbc70ae1dd225b097ee513f67d6ff5db97b8ee4345a","source":{"kind":"arxiv","id":"1801.10441","version":1},"attestation_state":"computed","paper":{"title":"Weighted Nonlocal Total Variation in Image Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.CV","authors_text":"Haohan Li, Xiaoping Wang, Zuoqiang Shi","submitted_at":"2018-01-31T13:24:34Z","abstract_excerpt":"In this paper, a novel weighted nonlocal total variation (WNTV) method is proposed. Compared to the classical nonlocal total variation methods, our method modifies the energy functional to introduce a weight to balance between the labeled sets and unlabeled sets. With extensive numerical examples in semi-supervised clustering, image inpainting and image colorization, we demonstrate that WNTV provides an effective and efficient method in many image processing and machine learning problems."},"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":"1801.10441","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-01-31T13:24:34Z","cross_cats_sorted":["math.OC"],"title_canon_sha256":"9c4daaed352c295dfcbc8661f5e25cedd163a58556b7b0e78121851112204a08","abstract_canon_sha256":"b93966c16fd64b475540583530e7518fa5c04528b51cfc3ee12fe2a8d29dd974"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:40.815629Z","signature_b64":"KqB4W3OsTGNrSN0lzMhUkkikVQegMYwyd9uJdN9LjjBg25+c2gzL+lXw0i50c/ZGk8KQ54u+8CGcqVo3g8qFDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90d373b5d19817e9f5e20dbc70ae1dd225b097ee513f67d6ff5db97b8ee4345a","last_reissued_at":"2026-05-18T00:24:40.814893Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:40.814893Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Weighted Nonlocal Total Variation in Image Processing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.OC"],"primary_cat":"cs.CV","authors_text":"Haohan Li, Xiaoping Wang, Zuoqiang Shi","submitted_at":"2018-01-31T13:24:34Z","abstract_excerpt":"In this paper, a novel weighted nonlocal total variation (WNTV) method is proposed. Compared to the classical nonlocal total variation methods, our method modifies the energy functional to introduce a weight to balance between the labeled sets and unlabeled sets. With extensive numerical examples in semi-supervised clustering, image inpainting and image colorization, we demonstrate that WNTV provides an effective and efficient method in many image processing and machine learning problems."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1801.10441","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":""},"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":"1801.10441","created_at":"2026-05-18T00:24:40.815006+00:00"},{"alias_kind":"arxiv_version","alias_value":"1801.10441v1","created_at":"2026-05-18T00:24:40.815006+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1801.10441","created_at":"2026-05-18T00:24:40.815006+00:00"},{"alias_kind":"pith_short_12","alias_value":"SDJXHNORTAL6","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_16","alias_value":"SDJXHNORTAL6T5PC","created_at":"2026-05-18T12:32:53.628368+00:00"},{"alias_kind":"pith_short_8","alias_value":"SDJXHNOR","created_at":"2026-05-18T12:32:53.628368+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/SDJXHNORTAL6T5PCBW6HBLQ52I","json":"https://pith.science/pith/SDJXHNORTAL6T5PCBW6HBLQ52I.json","graph_json":"https://pith.science/api/pith-number/SDJXHNORTAL6T5PCBW6HBLQ52I/graph.json","events_json":"https://pith.science/api/pith-number/SDJXHNORTAL6T5PCBW6HBLQ52I/events.json","paper":"https://pith.science/paper/SDJXHNOR"},"agent_actions":{"view_html":"https://pith.science/pith/SDJXHNORTAL6T5PCBW6HBLQ52I","download_json":"https://pith.science/pith/SDJXHNORTAL6T5PCBW6HBLQ52I.json","view_paper":"https://pith.science/paper/SDJXHNOR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1801.10441&json=true","fetch_graph":"https://pith.science/api/pith-number/SDJXHNORTAL6T5PCBW6HBLQ52I/graph.json","fetch_events":"https://pith.science/api/pith-number/SDJXHNORTAL6T5PCBW6HBLQ52I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SDJXHNORTAL6T5PCBW6HBLQ52I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SDJXHNORTAL6T5PCBW6HBLQ52I/action/storage_attestation","attest_author":"https://pith.science/pith/SDJXHNORTAL6T5PCBW6HBLQ52I/action/author_attestation","sign_citation":"https://pith.science/pith/SDJXHNORTAL6T5PCBW6HBLQ52I/action/citation_signature","submit_replication":"https://pith.science/pith/SDJXHNORTAL6T5PCBW6HBLQ52I/action/replication_record"}},"created_at":"2026-05-18T00:24:40.815006+00:00","updated_at":"2026-05-18T00:24:40.815006+00:00"}