{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HJFGYUEDXTQO73ADISF4VWV4S3","short_pith_number":"pith:HJFGYUED","schema_version":"1.0","canonical_sha256":"3a4a6c5083bce0efec03448bcadabc96f2151b493bc3fc1cedba06db12b91a31","source":{"kind":"arxiv","id":"2308.09426","version":1},"attestation_state":"computed","paper":{"title":"Self-Supervised Single-Image Deconvolution with Siamese Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Kaupo Palo, Leopold Parts, Mikhail Papkov","submitted_at":"2023-08-18T09:51:11Z","abstract_excerpt":"Inverse problems in image reconstruction are fundamentally complicated by unknown noise properties. Classical iterative deconvolution approaches amplify noise and require careful parameter selection for an optimal trade-off between sharpness and grain. Deep learning methods allow for flexible parametrization of the noise and learning its properties directly from the data. Recently, self-supervised blind-spot neural networks were successfully adopted for image deconvolution by including a known point-spread function in the end-to-end training. However, their practical application has been limit"},"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":"2308.09426","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-08-18T09:51:11Z","cross_cats_sorted":["cs.LG","eess.IV"],"title_canon_sha256":"06ec4ebf324dcc64dfba797588fa1075d8c15c525a1a8da391dadc24abe36034","abstract_canon_sha256":"d55e0233d2a4db4a2a4b4acf039d2910d653c71290a7ac7c70c2fd128604c4c0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:42:32.483196Z","signature_b64":"5UNbuEgQFWuS0S3qTVSmARGDgAHXJ7ycM09f4taY08+ZbOzpmZ56JqGd4TRhGWa5af+NMY2TOo/y3xroUlN+Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3a4a6c5083bce0efec03448bcadabc96f2151b493bc3fc1cedba06db12b91a31","last_reissued_at":"2026-07-05T06:42:32.482514Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:42:32.482514Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Self-Supervised Single-Image Deconvolution with Siamese Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV"],"primary_cat":"cs.CV","authors_text":"Kaupo Palo, Leopold Parts, Mikhail Papkov","submitted_at":"2023-08-18T09:51:11Z","abstract_excerpt":"Inverse problems in image reconstruction are fundamentally complicated by unknown noise properties. Classical iterative deconvolution approaches amplify noise and require careful parameter selection for an optimal trade-off between sharpness and grain. Deep learning methods allow for flexible parametrization of the noise and learning its properties directly from the data. Recently, self-supervised blind-spot neural networks were successfully adopted for image deconvolution by including a known point-spread function in the end-to-end training. However, their practical application has been limit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.09426","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2308.09426/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2308.09426","created_at":"2026-07-05T06:42:32.482601+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.09426v1","created_at":"2026-07-05T06:42:32.482601+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.09426","created_at":"2026-07-05T06:42:32.482601+00:00"},{"alias_kind":"pith_short_12","alias_value":"HJFGYUEDXTQO","created_at":"2026-07-05T06:42:32.482601+00:00"},{"alias_kind":"pith_short_16","alias_value":"HJFGYUEDXTQO73AD","created_at":"2026-07-05T06:42:32.482601+00:00"},{"alias_kind":"pith_short_8","alias_value":"HJFGYUED","created_at":"2026-07-05T06:42:32.482601+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/HJFGYUEDXTQO73ADISF4VWV4S3","json":"https://pith.science/pith/HJFGYUEDXTQO73ADISF4VWV4S3.json","graph_json":"https://pith.science/api/pith-number/HJFGYUEDXTQO73ADISF4VWV4S3/graph.json","events_json":"https://pith.science/api/pith-number/HJFGYUEDXTQO73ADISF4VWV4S3/events.json","paper":"https://pith.science/paper/HJFGYUED"},"agent_actions":{"view_html":"https://pith.science/pith/HJFGYUEDXTQO73ADISF4VWV4S3","download_json":"https://pith.science/pith/HJFGYUEDXTQO73ADISF4VWV4S3.json","view_paper":"https://pith.science/paper/HJFGYUED","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.09426&json=true","fetch_graph":"https://pith.science/api/pith-number/HJFGYUEDXTQO73ADISF4VWV4S3/graph.json","fetch_events":"https://pith.science/api/pith-number/HJFGYUEDXTQO73ADISF4VWV4S3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HJFGYUEDXTQO73ADISF4VWV4S3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HJFGYUEDXTQO73ADISF4VWV4S3/action/storage_attestation","attest_author":"https://pith.science/pith/HJFGYUEDXTQO73ADISF4VWV4S3/action/author_attestation","sign_citation":"https://pith.science/pith/HJFGYUEDXTQO73ADISF4VWV4S3/action/citation_signature","submit_replication":"https://pith.science/pith/HJFGYUEDXTQO73ADISF4VWV4S3/action/replication_record"}},"created_at":"2026-07-05T06:42:32.482601+00:00","updated_at":"2026-07-05T06:42:32.482601+00:00"}