{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:7GJ4WRY6VX3QFLA2KPMWF6KOXH","short_pith_number":"pith:7GJ4WRY6","schema_version":"1.0","canonical_sha256":"f993cb471eadf702ac1a53d962f94eb9fbc7336faaec0f129b7859415b0f5ac6","source":{"kind":"arxiv","id":"1209.1826","version":1},"attestation_state":"computed","paper":{"title":"A spatio-spectral hybridization for edge preservation and noisy image restoration via local parametric mixtures and Lagrangian relaxation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.AP"],"primary_cat":"stat.ME","authors_text":"Debapriya Sengupta, Kinjal Basu","submitted_at":"2012-09-09T18:23:21Z","abstract_excerpt":"This paper investigates a fully unsupervised statistical method for edge preserving image restoration and compression using a spatial decomposition scheme. Smoothed maximum likelihood is used for local estimation of edge pixels from mixture parametric models of local templates. For the complementary smooth part the traditional L2-variational problem is solved in the Fourier domain with Thin Plate Spline (TPS) regularization. It is well known that naive Fourier compression of the whole image fails to restore a piece-wise smooth noisy image satisfactorily due to Gibbs phenomenon. Images are inte"},"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":"1209.1826","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-09-09T18:23:21Z","cross_cats_sorted":["cs.CV","stat.AP"],"title_canon_sha256":"557f2f0ff21fb61de2950215876b199815fa2b380bbb9fa12842a05449ae607b","abstract_canon_sha256":"4cdeeb5f95f07f460f61dddc83251cd1aaedd04a8e8cfbfa996439af9c305ef5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:56:52.505122Z","signature_b64":"9gRPBwMYBptQ8eVCKRyzI9seCks01WXvQNNOKRKQBJgxy/QyyEx/vil9vB/F0GMR15jnub34qSJfyiw1YQUQDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f993cb471eadf702ac1a53d962f94eb9fbc7336faaec0f129b7859415b0f5ac6","last_reissued_at":"2026-05-18T00:56:52.504635Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:56:52.504635Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A spatio-spectral hybridization for edge preservation and noisy image restoration via local parametric mixtures and Lagrangian relaxation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","stat.AP"],"primary_cat":"stat.ME","authors_text":"Debapriya Sengupta, Kinjal Basu","submitted_at":"2012-09-09T18:23:21Z","abstract_excerpt":"This paper investigates a fully unsupervised statistical method for edge preserving image restoration and compression using a spatial decomposition scheme. Smoothed maximum likelihood is used for local estimation of edge pixels from mixture parametric models of local templates. For the complementary smooth part the traditional L2-variational problem is solved in the Fourier domain with Thin Plate Spline (TPS) regularization. It is well known that naive Fourier compression of the whole image fails to restore a piece-wise smooth noisy image satisfactorily due to Gibbs phenomenon. Images are inte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1209.1826","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":"1209.1826","created_at":"2026-05-18T00:56:52.504712+00:00"},{"alias_kind":"arxiv_version","alias_value":"1209.1826v1","created_at":"2026-05-18T00:56:52.504712+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1209.1826","created_at":"2026-05-18T00:56:52.504712+00:00"},{"alias_kind":"pith_short_12","alias_value":"7GJ4WRY6VX3Q","created_at":"2026-05-18T12:26:56.085431+00:00"},{"alias_kind":"pith_short_16","alias_value":"7GJ4WRY6VX3QFLA2","created_at":"2026-05-18T12:26:56.085431+00:00"},{"alias_kind":"pith_short_8","alias_value":"7GJ4WRY6","created_at":"2026-05-18T12:26:56.085431+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/7GJ4WRY6VX3QFLA2KPMWF6KOXH","json":"https://pith.science/pith/7GJ4WRY6VX3QFLA2KPMWF6KOXH.json","graph_json":"https://pith.science/api/pith-number/7GJ4WRY6VX3QFLA2KPMWF6KOXH/graph.json","events_json":"https://pith.science/api/pith-number/7GJ4WRY6VX3QFLA2KPMWF6KOXH/events.json","paper":"https://pith.science/paper/7GJ4WRY6"},"agent_actions":{"view_html":"https://pith.science/pith/7GJ4WRY6VX3QFLA2KPMWF6KOXH","download_json":"https://pith.science/pith/7GJ4WRY6VX3QFLA2KPMWF6KOXH.json","view_paper":"https://pith.science/paper/7GJ4WRY6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1209.1826&json=true","fetch_graph":"https://pith.science/api/pith-number/7GJ4WRY6VX3QFLA2KPMWF6KOXH/graph.json","fetch_events":"https://pith.science/api/pith-number/7GJ4WRY6VX3QFLA2KPMWF6KOXH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7GJ4WRY6VX3QFLA2KPMWF6KOXH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7GJ4WRY6VX3QFLA2KPMWF6KOXH/action/storage_attestation","attest_author":"https://pith.science/pith/7GJ4WRY6VX3QFLA2KPMWF6KOXH/action/author_attestation","sign_citation":"https://pith.science/pith/7GJ4WRY6VX3QFLA2KPMWF6KOXH/action/citation_signature","submit_replication":"https://pith.science/pith/7GJ4WRY6VX3QFLA2KPMWF6KOXH/action/replication_record"}},"created_at":"2026-05-18T00:56:52.504712+00:00","updated_at":"2026-05-18T00:56:52.504712+00:00"}