{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:UDSN6GIXYRTRN3GCI3JGG7FKRK","short_pith_number":"pith:UDSN6GIX","schema_version":"1.0","canonical_sha256":"a0e4df1917c46716ecc246d2637caa8ab7fc0424b22fa705a57b0615cb28951c","source":{"kind":"arxiv","id":"1307.5996","version":2},"attestation_state":"computed","paper":{"title":"Bayesian Fusion of Multi-Band Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an","stat.ME"],"primary_cat":"cs.CV","authors_text":"Jean-Yves Tourneret, Nicolas Dobigeon, Qi Wei","submitted_at":"2013-07-23T09:44:36Z","abstract_excerpt":"In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical consideration is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo"},"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":"1307.5996","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2013-07-23T09:44:36Z","cross_cats_sorted":["physics.data-an","stat.ME"],"title_canon_sha256":"579f97ec05da34ef0d4f2b87b7cbe67d2a67a69ada74505239e243c7dd4bafc3","abstract_canon_sha256":"6549593d6b1778060b3e052fb64d91da42adf8967629b99dbfd184c2ea5a1102"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:44:23.345329Z","signature_b64":"CksumQe88HSofOJ895G+LxhEtsklnXVCixaBkLIchyVSu/SLVS14Y45n3/Rb11R2szUUKwqfaAn6xMGwJY5RBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0e4df1917c46716ecc246d2637caa8ab7fc0424b22fa705a57b0615cb28951c","last_reissued_at":"2026-05-18T02:44:23.344716Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:44:23.344716Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Fusion of Multi-Band Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.data-an","stat.ME"],"primary_cat":"cs.CV","authors_text":"Jean-Yves Tourneret, Nicolas Dobigeon, Qi Wei","submitted_at":"2013-07-23T09:44:36Z","abstract_excerpt":"In this paper, a Bayesian fusion technique for remotely sensed multi-band images is presented. The observed images are related to the high spectral and high spatial resolution image to be recovered through physical degradations, e.g., spatial and spectral blurring and/or subsampling defined by the sensor characteristics. The fusion problem is formulated within a Bayesian estimation framework. An appropriate prior distribution exploiting geometrical consideration is introduced. To compute the Bayesian estimator of the scene of interest from its posterior distribution, a Markov chain Monte Carlo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1307.5996","kind":"arxiv","version":2},"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":"1307.5996","created_at":"2026-05-18T02:44:23.344794+00:00"},{"alias_kind":"arxiv_version","alias_value":"1307.5996v2","created_at":"2026-05-18T02:44:23.344794+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1307.5996","created_at":"2026-05-18T02:44:23.344794+00:00"},{"alias_kind":"pith_short_12","alias_value":"UDSN6GIXYRTR","created_at":"2026-05-18T12:28:02.375192+00:00"},{"alias_kind":"pith_short_16","alias_value":"UDSN6GIXYRTRN3GC","created_at":"2026-05-18T12:28:02.375192+00:00"},{"alias_kind":"pith_short_8","alias_value":"UDSN6GIX","created_at":"2026-05-18T12:28:02.375192+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/UDSN6GIXYRTRN3GCI3JGG7FKRK","json":"https://pith.science/pith/UDSN6GIXYRTRN3GCI3JGG7FKRK.json","graph_json":"https://pith.science/api/pith-number/UDSN6GIXYRTRN3GCI3JGG7FKRK/graph.json","events_json":"https://pith.science/api/pith-number/UDSN6GIXYRTRN3GCI3JGG7FKRK/events.json","paper":"https://pith.science/paper/UDSN6GIX"},"agent_actions":{"view_html":"https://pith.science/pith/UDSN6GIXYRTRN3GCI3JGG7FKRK","download_json":"https://pith.science/pith/UDSN6GIXYRTRN3GCI3JGG7FKRK.json","view_paper":"https://pith.science/paper/UDSN6GIX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1307.5996&json=true","fetch_graph":"https://pith.science/api/pith-number/UDSN6GIXYRTRN3GCI3JGG7FKRK/graph.json","fetch_events":"https://pith.science/api/pith-number/UDSN6GIXYRTRN3GCI3JGG7FKRK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UDSN6GIXYRTRN3GCI3JGG7FKRK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UDSN6GIXYRTRN3GCI3JGG7FKRK/action/storage_attestation","attest_author":"https://pith.science/pith/UDSN6GIXYRTRN3GCI3JGG7FKRK/action/author_attestation","sign_citation":"https://pith.science/pith/UDSN6GIXYRTRN3GCI3JGG7FKRK/action/citation_signature","submit_replication":"https://pith.science/pith/UDSN6GIXYRTRN3GCI3JGG7FKRK/action/replication_record"}},"created_at":"2026-05-18T02:44:23.344794+00:00","updated_at":"2026-05-18T02:44:23.344794+00:00"}