{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:GVBZ475GMAEL6OW2UM364CACPE","short_pith_number":"pith:GVBZ475G","schema_version":"1.0","canonical_sha256":"35439e7fa66008bf3adaa337ee08027912931b82e3d0324cc4756064d0d68b34","source":{"kind":"arxiv","id":"2407.20210","version":3},"attestation_state":"computed","paper":{"title":"An Efficient Image Denoising Method Integrating Multi-resolution Local Clustering and Adaptive Smoothing","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Partha Sarathi Mukherjee, Subhasish Basak","submitted_at":"2024-07-29T17:40:38Z","abstract_excerpt":"The importance of developing efficient image denoising methods is immense especially for modern applications such as image comparisons, image monitoring, medical image diagnostics, and so forth. Available methods in the vast literature on image denoising can address certain issues in image denoising, but no one single method can solve all such issues. For example, jump regression based methods can preserve linear edges well, but cannot preserve many other fine details of an image. On the other hand, local clustering based methods can preserve fine edge structures, but cannot perform well in pr"},"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":"2407.20210","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.AP","submitted_at":"2024-07-29T17:40:38Z","cross_cats_sorted":[],"title_canon_sha256":"8560af3ce01a6bf8fb2c1a11c57c9328bd987db05ee9afd5f274d39979f16b91","abstract_canon_sha256":"533ad217798f3a9da0c9d1305075ae542eab498493e0b8fd382c66c5135a0217"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:58:19.892864Z","signature_b64":"jmHeJ1AXEFilXHAxVCIA2k0FJ5FojLuoK07dSEcVeI2dVNcu28Ck/GT1O4Jef5gU4V58/RGeo6KDhcRh0WVbBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"35439e7fa66008bf3adaa337ee08027912931b82e3d0324cc4756064d0d68b34","last_reissued_at":"2026-07-05T11:58:19.892352Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:58:19.892352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Efficient Image Denoising Method Integrating Multi-resolution Local Clustering and Adaptive Smoothing","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Partha Sarathi Mukherjee, Subhasish Basak","submitted_at":"2024-07-29T17:40:38Z","abstract_excerpt":"The importance of developing efficient image denoising methods is immense especially for modern applications such as image comparisons, image monitoring, medical image diagnostics, and so forth. Available methods in the vast literature on image denoising can address certain issues in image denoising, but no one single method can solve all such issues. For example, jump regression based methods can preserve linear edges well, but cannot preserve many other fine details of an image. On the other hand, local clustering based methods can preserve fine edge structures, but cannot perform well in pr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.20210","kind":"arxiv","version":3},"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/2407.20210/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":"2407.20210","created_at":"2026-07-05T11:58:19.892409+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.20210v3","created_at":"2026-07-05T11:58:19.892409+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.20210","created_at":"2026-07-05T11:58:19.892409+00:00"},{"alias_kind":"pith_short_12","alias_value":"GVBZ475GMAEL","created_at":"2026-07-05T11:58:19.892409+00:00"},{"alias_kind":"pith_short_16","alias_value":"GVBZ475GMAEL6OW2","created_at":"2026-07-05T11:58:19.892409+00:00"},{"alias_kind":"pith_short_8","alias_value":"GVBZ475G","created_at":"2026-07-05T11:58:19.892409+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/GVBZ475GMAEL6OW2UM364CACPE","json":"https://pith.science/pith/GVBZ475GMAEL6OW2UM364CACPE.json","graph_json":"https://pith.science/api/pith-number/GVBZ475GMAEL6OW2UM364CACPE/graph.json","events_json":"https://pith.science/api/pith-number/GVBZ475GMAEL6OW2UM364CACPE/events.json","paper":"https://pith.science/paper/GVBZ475G"},"agent_actions":{"view_html":"https://pith.science/pith/GVBZ475GMAEL6OW2UM364CACPE","download_json":"https://pith.science/pith/GVBZ475GMAEL6OW2UM364CACPE.json","view_paper":"https://pith.science/paper/GVBZ475G","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.20210&json=true","fetch_graph":"https://pith.science/api/pith-number/GVBZ475GMAEL6OW2UM364CACPE/graph.json","fetch_events":"https://pith.science/api/pith-number/GVBZ475GMAEL6OW2UM364CACPE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GVBZ475GMAEL6OW2UM364CACPE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GVBZ475GMAEL6OW2UM364CACPE/action/storage_attestation","attest_author":"https://pith.science/pith/GVBZ475GMAEL6OW2UM364CACPE/action/author_attestation","sign_citation":"https://pith.science/pith/GVBZ475GMAEL6OW2UM364CACPE/action/citation_signature","submit_replication":"https://pith.science/pith/GVBZ475GMAEL6OW2UM364CACPE/action/replication_record"}},"created_at":"2026-07-05T11:58:19.892409+00:00","updated_at":"2026-07-05T11:58:19.892409+00:00"}