{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:TQJJ62Y6ACVWXVRA2EED5NWWFZ","short_pith_number":"pith:TQJJ62Y6","schema_version":"1.0","canonical_sha256":"9c129f6b1e00ab6bd620d1083eb6d62e459a305b75c1b458a0264ea1fddde642","source":{"kind":"arxiv","id":"1706.03261","version":1},"attestation_state":"computed","paper":{"title":"A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV","stat.ML"],"primary_cat":"cs.CV","authors_text":"Andr\\'es Almansa, Cecilia Aguerrebere, Julie Delon, Pablo Mus\\'e, Yann Gousseau","submitted_at":"2017-06-10T17:37:01Z","abstract_excerpt":"Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems"},"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":"1706.03261","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-10T17:37:01Z","cross_cats_sorted":["eess.IV","stat.ML"],"title_canon_sha256":"b9bb42aac40b8a5f4dfc5e4d1e05695ebc8f4f8741478efec370374f20566706","abstract_canon_sha256":"c25fbd2e95ef0de3253b9e2ab775c97753a2e3136b112559dc78fbbc808b145d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:28:35.404073Z","signature_b64":"zPqqYp0CsOFVW7RuO3n0pt2Tqz74pubUPgjCu0EyoDEdiw/Vl6ao4tcl3Ar3+qVQKo47Fc1KsPD02PP+dSXJDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9c129f6b1e00ab6bd620d1083eb6d62e459a305b75c1b458a0264ea1fddde642","last_reissued_at":"2026-05-18T00:28:35.403398Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:28:35.403398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Bayesian Hyperprior Approach for Joint Image Denoising and Interpolation, with an Application to HDR Imaging","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV","stat.ML"],"primary_cat":"cs.CV","authors_text":"Andr\\'es Almansa, Cecilia Aguerrebere, Julie Delon, Pablo Mus\\'e, Yann Gousseau","submitted_at":"2017-06-10T17:37:01Z","abstract_excerpt":"Recently, impressive denoising results have been achieved by Bayesian approaches which assume Gaussian models for the image patches. This improvement in performance can be attributed to the use of per-patch models. Unfortunately such an approach is particularly unstable for most inverse problems beyond denoising. In this work, we propose the use of a hyperprior to model image patches, in order to stabilize the estimation procedure. There are two main advantages to the proposed restoration scheme: Firstly it is adapted to diagonal degradation matrices, and in particular to missing data problems"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.03261","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":"1706.03261","created_at":"2026-05-18T00:28:35.403507+00:00"},{"alias_kind":"arxiv_version","alias_value":"1706.03261v1","created_at":"2026-05-18T00:28:35.403507+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.03261","created_at":"2026-05-18T00:28:35.403507+00:00"},{"alias_kind":"pith_short_12","alias_value":"TQJJ62Y6ACVW","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_16","alias_value":"TQJJ62Y6ACVWXVRA","created_at":"2026-05-18T12:31:46.661854+00:00"},{"alias_kind":"pith_short_8","alias_value":"TQJJ62Y6","created_at":"2026-05-18T12:31:46.661854+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/TQJJ62Y6ACVWXVRA2EED5NWWFZ","json":"https://pith.science/pith/TQJJ62Y6ACVWXVRA2EED5NWWFZ.json","graph_json":"https://pith.science/api/pith-number/TQJJ62Y6ACVWXVRA2EED5NWWFZ/graph.json","events_json":"https://pith.science/api/pith-number/TQJJ62Y6ACVWXVRA2EED5NWWFZ/events.json","paper":"https://pith.science/paper/TQJJ62Y6"},"agent_actions":{"view_html":"https://pith.science/pith/TQJJ62Y6ACVWXVRA2EED5NWWFZ","download_json":"https://pith.science/pith/TQJJ62Y6ACVWXVRA2EED5NWWFZ.json","view_paper":"https://pith.science/paper/TQJJ62Y6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1706.03261&json=true","fetch_graph":"https://pith.science/api/pith-number/TQJJ62Y6ACVWXVRA2EED5NWWFZ/graph.json","fetch_events":"https://pith.science/api/pith-number/TQJJ62Y6ACVWXVRA2EED5NWWFZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TQJJ62Y6ACVWXVRA2EED5NWWFZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TQJJ62Y6ACVWXVRA2EED5NWWFZ/action/storage_attestation","attest_author":"https://pith.science/pith/TQJJ62Y6ACVWXVRA2EED5NWWFZ/action/author_attestation","sign_citation":"https://pith.science/pith/TQJJ62Y6ACVWXVRA2EED5NWWFZ/action/citation_signature","submit_replication":"https://pith.science/pith/TQJJ62Y6ACVWXVRA2EED5NWWFZ/action/replication_record"}},"created_at":"2026-05-18T00:28:35.403507+00:00","updated_at":"2026-05-18T00:28:35.403507+00:00"}