{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:ODXDUCTFFBI5K6FOQJDTIQHCDO","short_pith_number":"pith:ODXDUCTF","schema_version":"1.0","canonical_sha256":"70ee3a0a652851d578ae82473440e21bbe8f432b4fa87f5cf2b93b5495732fe8","source":{"kind":"arxiv","id":"1403.2482","version":1},"attestation_state":"computed","paper":{"title":"Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Li, Haijuan Hu, Quansheng Liu","submitted_at":"2014-03-11T06:48:58Z","abstract_excerpt":"We first establish a law of large numbers and a convergence theorem in distribution to show the rate of convergence of the non-local means filter for removing Gaussian noise. We then introduce the notion of degree of similarity to measure the role of similarity for the non-local means filter. Based on the convergence theorems, we propose a patch-based weighted means filter for removing impulse noise and its mixture with Gaussian noise by combining the essential idea of the trilateral filter and that of the non-local means filter. Our experiments show that our filter is competitive compared to "},"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":"1403.2482","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2014-03-11T06:48:58Z","cross_cats_sorted":[],"title_canon_sha256":"678ba3bc14f7e077f63d9719152d6a04265b61a8749a5c6fd1bcc8d880f29726","abstract_canon_sha256":"dbb9aeec82a371794ea94b9b7b5d02aa93f9e0fe0d3b60d0a7a092e0436dcb49"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:56:43.218986Z","signature_b64":"BRid1xou9cdrlYFpa6CHktQ9V0IJ0nwOV9UBWexgaNe//gfjjIE//i0mWZgP38liQg0WNsKEECflEzyzSGl4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70ee3a0a652851d578ae82473440e21bbe8f432b4fa87f5cf2b93b5495732fe8","last_reissued_at":"2026-05-18T02:56:43.218533Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:56:43.218533Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Removing Mixture of Gaussian and Impulse Noise by Patch-Based Weighted Means","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Li, Haijuan Hu, Quansheng Liu","submitted_at":"2014-03-11T06:48:58Z","abstract_excerpt":"We first establish a law of large numbers and a convergence theorem in distribution to show the rate of convergence of the non-local means filter for removing Gaussian noise. We then introduce the notion of degree of similarity to measure the role of similarity for the non-local means filter. Based on the convergence theorems, we propose a patch-based weighted means filter for removing impulse noise and its mixture with Gaussian noise by combining the essential idea of the trilateral filter and that of the non-local means filter. Our experiments show that our filter is competitive compared to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.2482","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":"1403.2482","created_at":"2026-05-18T02:56:43.218599+00:00"},{"alias_kind":"arxiv_version","alias_value":"1403.2482v1","created_at":"2026-05-18T02:56:43.218599+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.2482","created_at":"2026-05-18T02:56:43.218599+00:00"},{"alias_kind":"pith_short_12","alias_value":"ODXDUCTFFBI5","created_at":"2026-05-18T12:28:41.024544+00:00"},{"alias_kind":"pith_short_16","alias_value":"ODXDUCTFFBI5K6FO","created_at":"2026-05-18T12:28:41.024544+00:00"},{"alias_kind":"pith_short_8","alias_value":"ODXDUCTF","created_at":"2026-05-18T12:28:41.024544+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/ODXDUCTFFBI5K6FOQJDTIQHCDO","json":"https://pith.science/pith/ODXDUCTFFBI5K6FOQJDTIQHCDO.json","graph_json":"https://pith.science/api/pith-number/ODXDUCTFFBI5K6FOQJDTIQHCDO/graph.json","events_json":"https://pith.science/api/pith-number/ODXDUCTFFBI5K6FOQJDTIQHCDO/events.json","paper":"https://pith.science/paper/ODXDUCTF"},"agent_actions":{"view_html":"https://pith.science/pith/ODXDUCTFFBI5K6FOQJDTIQHCDO","download_json":"https://pith.science/pith/ODXDUCTFFBI5K6FOQJDTIQHCDO.json","view_paper":"https://pith.science/paper/ODXDUCTF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1403.2482&json=true","fetch_graph":"https://pith.science/api/pith-number/ODXDUCTFFBI5K6FOQJDTIQHCDO/graph.json","fetch_events":"https://pith.science/api/pith-number/ODXDUCTFFBI5K6FOQJDTIQHCDO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ODXDUCTFFBI5K6FOQJDTIQHCDO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ODXDUCTFFBI5K6FOQJDTIQHCDO/action/storage_attestation","attest_author":"https://pith.science/pith/ODXDUCTFFBI5K6FOQJDTIQHCDO/action/author_attestation","sign_citation":"https://pith.science/pith/ODXDUCTFFBI5K6FOQJDTIQHCDO/action/citation_signature","submit_replication":"https://pith.science/pith/ODXDUCTFFBI5K6FOQJDTIQHCDO/action/replication_record"}},"created_at":"2026-05-18T02:56:43.218599+00:00","updated_at":"2026-05-18T02:56:43.218599+00:00"}