{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:XFEKWKGPI6IW3VSGPH4GJAFQJA","short_pith_number":"pith:XFEKWKGP","schema_version":"1.0","canonical_sha256":"b948ab28cf47916dd64679f86480b048313627232e60d1f9980944055f16a9a6","source":{"kind":"arxiv","id":"2204.04754","version":3},"attestation_state":"computed","paper":{"title":"Denoiser-based projections for 2-D super-resolution multi-reference alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"eess.IV","authors_text":"Jonathan Shani, Raja Giryes, Tamir Bendory, Tom Tirer","submitted_at":"2022-04-10T19:29:07Z","abstract_excerpt":"We study the 2-D super-resolution multi-reference alignment (SR-MRA) problem: estimating an image from its down-sampled, circularly-translated, and noisy copies. The SR-MRA problem serves as a mathematical abstraction of the structure determination problem for biological molecules. Since the SR-MRA problem is ill-posed without prior knowledge, accurate image estimation relies on designing priors that well-describe the statistics of the images of interest. In this work, we build on recent advances in image processing, and harness the power of denoisers as priors of images. In particular, we sug"},"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":"2204.04754","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-04-10T19:29:07Z","cross_cats_sorted":["eess.SP"],"title_canon_sha256":"c2b9c1e04ddd2659a8653c87c6d4818cfc23518f9e508dcbb2a45210f25ed80e","abstract_canon_sha256":"6872dd5579bbae8d3f79a4b2a56e78e2f35bb3f1f25bb99fefe1fa557092171f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:16.685176Z","signature_b64":"nZAlhajXcIjkTnW+sQdCmvHurwkI8PE3vyEWuwxaYlmc8GO0058H9732Se/GMAauhBvaKFoFZdli8vAPmk0VCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b948ab28cf47916dd64679f86480b048313627232e60d1f9980944055f16a9a6","last_reissued_at":"2026-07-05T08:14:16.684795Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:16.684795Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Denoiser-based projections for 2-D super-resolution multi-reference alignment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.SP"],"primary_cat":"eess.IV","authors_text":"Jonathan Shani, Raja Giryes, Tamir Bendory, Tom Tirer","submitted_at":"2022-04-10T19:29:07Z","abstract_excerpt":"We study the 2-D super-resolution multi-reference alignment (SR-MRA) problem: estimating an image from its down-sampled, circularly-translated, and noisy copies. The SR-MRA problem serves as a mathematical abstraction of the structure determination problem for biological molecules. Since the SR-MRA problem is ill-posed without prior knowledge, accurate image estimation relies on designing priors that well-describe the statistics of the images of interest. In this work, we build on recent advances in image processing, and harness the power of denoisers as priors of images. In particular, we sug"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.04754","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2204.04754/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":"2204.04754","created_at":"2026-07-05T08:14:16.684847+00:00"},{"alias_kind":"arxiv_version","alias_value":"2204.04754v3","created_at":"2026-07-05T08:14:16.684847+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.04754","created_at":"2026-07-05T08:14:16.684847+00:00"},{"alias_kind":"pith_short_12","alias_value":"XFEKWKGPI6IW","created_at":"2026-07-05T08:14:16.684847+00:00"},{"alias_kind":"pith_short_16","alias_value":"XFEKWKGPI6IW3VSG","created_at":"2026-07-05T08:14:16.684847+00:00"},{"alias_kind":"pith_short_8","alias_value":"XFEKWKGP","created_at":"2026-07-05T08:14:16.684847+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/XFEKWKGPI6IW3VSGPH4GJAFQJA","json":"https://pith.science/pith/XFEKWKGPI6IW3VSGPH4GJAFQJA.json","graph_json":"https://pith.science/api/pith-number/XFEKWKGPI6IW3VSGPH4GJAFQJA/graph.json","events_json":"https://pith.science/api/pith-number/XFEKWKGPI6IW3VSGPH4GJAFQJA/events.json","paper":"https://pith.science/paper/XFEKWKGP"},"agent_actions":{"view_html":"https://pith.science/pith/XFEKWKGPI6IW3VSGPH4GJAFQJA","download_json":"https://pith.science/pith/XFEKWKGPI6IW3VSGPH4GJAFQJA.json","view_paper":"https://pith.science/paper/XFEKWKGP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2204.04754&json=true","fetch_graph":"https://pith.science/api/pith-number/XFEKWKGPI6IW3VSGPH4GJAFQJA/graph.json","fetch_events":"https://pith.science/api/pith-number/XFEKWKGPI6IW3VSGPH4GJAFQJA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/XFEKWKGPI6IW3VSGPH4GJAFQJA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/XFEKWKGPI6IW3VSGPH4GJAFQJA/action/storage_attestation","attest_author":"https://pith.science/pith/XFEKWKGPI6IW3VSGPH4GJAFQJA/action/author_attestation","sign_citation":"https://pith.science/pith/XFEKWKGPI6IW3VSGPH4GJAFQJA/action/citation_signature","submit_replication":"https://pith.science/pith/XFEKWKGPI6IW3VSGPH4GJAFQJA/action/replication_record"}},"created_at":"2026-07-05T08:14:16.684847+00:00","updated_at":"2026-07-05T08:14:16.684847+00:00"}