{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:IIKZXI7W5Q5JZ36PJ3WSDJTFI6","short_pith_number":"pith:IIKZXI7W","schema_version":"1.0","canonical_sha256":"42159ba3f6ec3a9cefcf4eed21a66547b514a2895377804a23feb008feb4e70b","source":{"kind":"arxiv","id":"1603.08720","version":1},"attestation_state":"computed","paper":{"title":"Multi-Band Image Fusion Based on Spectral Unmixing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jean-Yves Tourneret, Jose Bioucas-Dias, Marcus Chen, Nicolas Dobigeon, Qi Wei, Simon Godsill","submitted_at":"2016-03-29T10:54:21Z","abstract_excerpt":"This paper presents a multi-band image fusion algorithm based on unsupervised spectral unmixing for combining a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The widely used linear observation model (with additive Gaussian noise) is combined with the linear spectral mixture model to form the likelihoods of the observations. The non-negativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem. The joint fusion and unmixing problem is"},"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":"1603.08720","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-03-29T10:54:21Z","cross_cats_sorted":[],"title_canon_sha256":"7254c2dc23c9617678f52f3f44dab9aa3e1eb8f924508df6166286095c3f1864","abstract_canon_sha256":"67c9962f9afedb706cd0f263caee879217c65f061b0f842ab5e55045c10aecad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:00:39.496362Z","signature_b64":"wKJoDl2j9MWaXk5u25VDI0fxXWrKuvyDM+weSA5RVXcw+EXl0kL3WKW5rUP55q1hNK14FiyaIu8HTANfgSRkCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42159ba3f6ec3a9cefcf4eed21a66547b514a2895377804a23feb008feb4e70b","last_reissued_at":"2026-05-18T01:00:39.495891Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:00:39.495891Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Multi-Band Image Fusion Based on Spectral Unmixing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jean-Yves Tourneret, Jose Bioucas-Dias, Marcus Chen, Nicolas Dobigeon, Qi Wei, Simon Godsill","submitted_at":"2016-03-29T10:54:21Z","abstract_excerpt":"This paper presents a multi-band image fusion algorithm based on unsupervised spectral unmixing for combining a high-spatial low-spectral resolution image and a low-spatial high-spectral resolution image. The widely used linear observation model (with additive Gaussian noise) is combined with the linear spectral mixture model to form the likelihoods of the observations. The non-negativity and sum-to-one constraints resulting from the intrinsic physical properties of the abundances are introduced as prior information to regularize this ill-posed problem. The joint fusion and unmixing problem is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.08720","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":"1603.08720","created_at":"2026-05-18T01:00:39.495966+00:00"},{"alias_kind":"arxiv_version","alias_value":"1603.08720v1","created_at":"2026-05-18T01:00:39.495966+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.08720","created_at":"2026-05-18T01:00:39.495966+00:00"},{"alias_kind":"pith_short_12","alias_value":"IIKZXI7W5Q5J","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_16","alias_value":"IIKZXI7W5Q5JZ36P","created_at":"2026-05-18T12:30:22.444734+00:00"},{"alias_kind":"pith_short_8","alias_value":"IIKZXI7W","created_at":"2026-05-18T12:30:22.444734+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/IIKZXI7W5Q5JZ36PJ3WSDJTFI6","json":"https://pith.science/pith/IIKZXI7W5Q5JZ36PJ3WSDJTFI6.json","graph_json":"https://pith.science/api/pith-number/IIKZXI7W5Q5JZ36PJ3WSDJTFI6/graph.json","events_json":"https://pith.science/api/pith-number/IIKZXI7W5Q5JZ36PJ3WSDJTFI6/events.json","paper":"https://pith.science/paper/IIKZXI7W"},"agent_actions":{"view_html":"https://pith.science/pith/IIKZXI7W5Q5JZ36PJ3WSDJTFI6","download_json":"https://pith.science/pith/IIKZXI7W5Q5JZ36PJ3WSDJTFI6.json","view_paper":"https://pith.science/paper/IIKZXI7W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1603.08720&json=true","fetch_graph":"https://pith.science/api/pith-number/IIKZXI7W5Q5JZ36PJ3WSDJTFI6/graph.json","fetch_events":"https://pith.science/api/pith-number/IIKZXI7W5Q5JZ36PJ3WSDJTFI6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IIKZXI7W5Q5JZ36PJ3WSDJTFI6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IIKZXI7W5Q5JZ36PJ3WSDJTFI6/action/storage_attestation","attest_author":"https://pith.science/pith/IIKZXI7W5Q5JZ36PJ3WSDJTFI6/action/author_attestation","sign_citation":"https://pith.science/pith/IIKZXI7W5Q5JZ36PJ3WSDJTFI6/action/citation_signature","submit_replication":"https://pith.science/pith/IIKZXI7W5Q5JZ36PJ3WSDJTFI6/action/replication_record"}},"created_at":"2026-05-18T01:00:39.495966+00:00","updated_at":"2026-05-18T01:00:39.495966+00:00"}