{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:N2XS5PO53Y7POSI63TON3FGHSL","short_pith_number":"pith:N2XS5PO5","schema_version":"1.0","canonical_sha256":"6eaf2ebdddde3ef7491edcdcdd94c792c0fc033f5420015094f4d332d8a67f88","source":{"kind":"arxiv","id":"1701.07422","version":3},"attestation_state":"computed","paper":{"title":"A Convex Similarity Index for Sparse Recovery of Missing Image Samples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Amirhossein Javaheri, Farokh Marvasti, Hadi Zayyani","submitted_at":"2017-01-25T18:49:45Z","abstract_excerpt":"This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted especially for image signals. Instead of $l_2$-norm or Mean Square Error (MSE), a new perceptual quality measure is used as the similarity criterion between the original and the reconstructed images. The proposed criterion called Convex SIMilarity (CSIM) index is a modified version of the Structural SIMilarity (SSIM) index, which despite its predecessor, is convex and uni-modal. We derive mathematical properties for the proposed index and show how to optimally choose the param"},"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":"1701.07422","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2017-01-25T18:49:45Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"58841ddb139dbf46718d4fbad61742517d1d26757e0261747bf9f07dd9cc0633","abstract_canon_sha256":"43dfcac0268dd173656012ab6145db0539b3f18c732a5beeb6ef4095abcd2546"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:43.893807Z","signature_b64":"PBEN5r5MfUebNVUf0UWiCqlejfldR+1TqgnhLdKlYkfw958pG4cj92IMNzMURt10zYJ+p52916V5OKqhkJCSCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6eaf2ebdddde3ef7491edcdcdd94c792c0fc033f5420015094f4d332d8a67f88","last_reissued_at":"2026-05-18T00:32:43.893062Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:43.893062Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Convex Similarity Index for Sparse Recovery of Missing Image Samples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Amirhossein Javaheri, Farokh Marvasti, Hadi Zayyani","submitted_at":"2017-01-25T18:49:45Z","abstract_excerpt":"This paper investigates the problem of recovering missing samples using methods based on sparse representation adapted especially for image signals. Instead of $l_2$-norm or Mean Square Error (MSE), a new perceptual quality measure is used as the similarity criterion between the original and the reconstructed images. The proposed criterion called Convex SIMilarity (CSIM) index is a modified version of the Structural SIMilarity (SSIM) index, which despite its predecessor, is convex and uni-modal. We derive mathematical properties for the proposed index and show how to optimally choose the param"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.07422","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":""},"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":"1701.07422","created_at":"2026-05-18T00:32:43.893166+00:00"},{"alias_kind":"arxiv_version","alias_value":"1701.07422v3","created_at":"2026-05-18T00:32:43.893166+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.07422","created_at":"2026-05-18T00:32:43.893166+00:00"},{"alias_kind":"pith_short_12","alias_value":"N2XS5PO53Y7P","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_16","alias_value":"N2XS5PO53Y7POSI6","created_at":"2026-05-18T12:31:31.346846+00:00"},{"alias_kind":"pith_short_8","alias_value":"N2XS5PO5","created_at":"2026-05-18T12:31:31.346846+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/N2XS5PO53Y7POSI63TON3FGHSL","json":"https://pith.science/pith/N2XS5PO53Y7POSI63TON3FGHSL.json","graph_json":"https://pith.science/api/pith-number/N2XS5PO53Y7POSI63TON3FGHSL/graph.json","events_json":"https://pith.science/api/pith-number/N2XS5PO53Y7POSI63TON3FGHSL/events.json","paper":"https://pith.science/paper/N2XS5PO5"},"agent_actions":{"view_html":"https://pith.science/pith/N2XS5PO53Y7POSI63TON3FGHSL","download_json":"https://pith.science/pith/N2XS5PO53Y7POSI63TON3FGHSL.json","view_paper":"https://pith.science/paper/N2XS5PO5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1701.07422&json=true","fetch_graph":"https://pith.science/api/pith-number/N2XS5PO53Y7POSI63TON3FGHSL/graph.json","fetch_events":"https://pith.science/api/pith-number/N2XS5PO53Y7POSI63TON3FGHSL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N2XS5PO53Y7POSI63TON3FGHSL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N2XS5PO53Y7POSI63TON3FGHSL/action/storage_attestation","attest_author":"https://pith.science/pith/N2XS5PO53Y7POSI63TON3FGHSL/action/author_attestation","sign_citation":"https://pith.science/pith/N2XS5PO53Y7POSI63TON3FGHSL/action/citation_signature","submit_replication":"https://pith.science/pith/N2XS5PO53Y7POSI63TON3FGHSL/action/replication_record"}},"created_at":"2026-05-18T00:32:43.893166+00:00","updated_at":"2026-05-18T00:32:43.893166+00:00"}