{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:RR4WM2CDQ5SPVNGMKECHMAUVYL","short_pith_number":"pith:RR4WM2CD","schema_version":"1.0","canonical_sha256":"8c796668438764fab4cc5104760295c2dd75ef4d84effb9faff557dd47bea4ec","source":{"kind":"arxiv","id":"1807.04813","version":1},"attestation_state":"computed","paper":{"title":"Optimal Physical Preprocessing for Example-Based Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Alexander Robey, Vidya Ganapati","submitted_at":"2018-07-12T20:49:23Z","abstract_excerpt":"In example-based super-resolution, the function relating low-resolution images to their high-resolution counterparts is learned from a given dataset. This data-driven approach to solving the inverse problem of increasing image resolution has been implemented with deep learning algorithms. In this work, we explore modifying the imaging hardware in order to collect more informative low-resolution images for better ultimate high-resolution image reconstruction. We show that this \"physical preprocessing\" allows for improved image reconstruction with deep learning in Fourier ptychographic microscop"},"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":"1807.04813","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2018-07-12T20:49:23Z","cross_cats_sorted":[],"title_canon_sha256":"f2dc0284908be58192c097186ddbd7b9cde59a3950eacea0bd4cc72f6cc1d42a","abstract_canon_sha256":"51d61ee1c5b0af1b604e2947f42c0e9bc0b9bd4fbf995c0603449a2f5f261b04"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:08.041394Z","signature_b64":"MCUN38liiZZOymIBh5buYJZiZF6Ua4xDyCZIi+Ru5LCO4wlIfjmiIhYBS8lxuZQM09bDds58BOyKISYtzZRiDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8c796668438764fab4cc5104760295c2dd75ef4d84effb9faff557dd47bea4ec","last_reissued_at":"2026-05-17T23:59:08.040933Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:08.040933Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimal Physical Preprocessing for Example-Based Super-Resolution","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Alexander Robey, Vidya Ganapati","submitted_at":"2018-07-12T20:49:23Z","abstract_excerpt":"In example-based super-resolution, the function relating low-resolution images to their high-resolution counterparts is learned from a given dataset. This data-driven approach to solving the inverse problem of increasing image resolution has been implemented with deep learning algorithms. In this work, we explore modifying the imaging hardware in order to collect more informative low-resolution images for better ultimate high-resolution image reconstruction. We show that this \"physical preprocessing\" allows for improved image reconstruction with deep learning in Fourier ptychographic microscop"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.04813","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":"1807.04813","created_at":"2026-05-17T23:59:08.041005+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.04813v1","created_at":"2026-05-17T23:59:08.041005+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.04813","created_at":"2026-05-17T23:59:08.041005+00:00"},{"alias_kind":"pith_short_12","alias_value":"RR4WM2CDQ5SP","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_16","alias_value":"RR4WM2CDQ5SPVNGM","created_at":"2026-05-18T12:32:50.500415+00:00"},{"alias_kind":"pith_short_8","alias_value":"RR4WM2CD","created_at":"2026-05-18T12:32:50.500415+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/RR4WM2CDQ5SPVNGMKECHMAUVYL","json":"https://pith.science/pith/RR4WM2CDQ5SPVNGMKECHMAUVYL.json","graph_json":"https://pith.science/api/pith-number/RR4WM2CDQ5SPVNGMKECHMAUVYL/graph.json","events_json":"https://pith.science/api/pith-number/RR4WM2CDQ5SPVNGMKECHMAUVYL/events.json","paper":"https://pith.science/paper/RR4WM2CD"},"agent_actions":{"view_html":"https://pith.science/pith/RR4WM2CDQ5SPVNGMKECHMAUVYL","download_json":"https://pith.science/pith/RR4WM2CDQ5SPVNGMKECHMAUVYL.json","view_paper":"https://pith.science/paper/RR4WM2CD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.04813&json=true","fetch_graph":"https://pith.science/api/pith-number/RR4WM2CDQ5SPVNGMKECHMAUVYL/graph.json","fetch_events":"https://pith.science/api/pith-number/RR4WM2CDQ5SPVNGMKECHMAUVYL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RR4WM2CDQ5SPVNGMKECHMAUVYL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RR4WM2CDQ5SPVNGMKECHMAUVYL/action/storage_attestation","attest_author":"https://pith.science/pith/RR4WM2CDQ5SPVNGMKECHMAUVYL/action/author_attestation","sign_citation":"https://pith.science/pith/RR4WM2CDQ5SPVNGMKECHMAUVYL/action/citation_signature","submit_replication":"https://pith.science/pith/RR4WM2CDQ5SPVNGMKECHMAUVYL/action/replication_record"}},"created_at":"2026-05-17T23:59:08.041005+00:00","updated_at":"2026-05-17T23:59:08.041005+00:00"}