{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:5GKKQH3P35E4FDFDKTDE37H5PT","short_pith_number":"pith:5GKKQH3P","schema_version":"1.0","canonical_sha256":"e994a81f6fdf49c28ca354c64dfcfd7cf10012d0b6771e5bae83fa5ca33de3fa","source":{"kind":"arxiv","id":"1908.09414","version":3},"attestation_state":"computed","paper":{"title":"CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Hyoungjun Park, Jong Chul Ye, Sang-Eun Lee, Sunghoe Chang, Sungjun Lim","submitted_at":"2019-08-26T00:34:16Z","abstract_excerpt":"Deconvolution microscopy has been extensively used to improve the resolution of the wide-field fluorescent microscopy, but the performance of classical approaches critically depends on the accuracy of a model and optimization algorithms. Recently, the convolutional neural network (CNN) approaches have been studied as a fast and high performance alternative. Unfortunately, the CNN approaches usually require matched high resolution images for supervised training. In this paper, we present a novel unsupervised cycle-consistent generative adversarial network (cycleGAN) with a linear blur kernel, w"},"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":"1908.09414","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-08-26T00:34:16Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"c423dc957c23a8f67207d57d46bc408c7beb7a20335e3ab29f459f98dc781a00","abstract_canon_sha256":"b32a705fb2ce6a7457e8c3c98c86944c075c64d90b4dc849cc6954cd02664c88"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:17:06.405433Z","signature_b64":"wpz+NVft6nJyeqakHtHuJrZ+wT3NKsupESHcf0XnCytPPnZ1eZmhlFQ1Fay/A64ZvdVj6t+A4ij44xMD85xWCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e994a81f6fdf49c28ca354c64dfcfd7cf10012d0b6771e5bae83fa5ca33de3fa","last_reissued_at":"2026-07-05T01:17:06.404857Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:17:06.404857Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CycleGAN with a Blur Kernel for Deconvolution Microscopy: Optimal Transport Geometry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Hyoungjun Park, Jong Chul Ye, Sang-Eun Lee, Sunghoe Chang, Sungjun Lim","submitted_at":"2019-08-26T00:34:16Z","abstract_excerpt":"Deconvolution microscopy has been extensively used to improve the resolution of the wide-field fluorescent microscopy, but the performance of classical approaches critically depends on the accuracy of a model and optimization algorithms. Recently, the convolutional neural network (CNN) approaches have been studied as a fast and high performance alternative. Unfortunately, the CNN approaches usually require matched high resolution images for supervised training. In this paper, we present a novel unsupervised cycle-consistent generative adversarial network (cycleGAN) with a linear blur kernel, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.09414","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/1908.09414/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":"1908.09414","created_at":"2026-07-05T01:17:06.404922+00:00"},{"alias_kind":"arxiv_version","alias_value":"1908.09414v3","created_at":"2026-07-05T01:17:06.404922+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.09414","created_at":"2026-07-05T01:17:06.404922+00:00"},{"alias_kind":"pith_short_12","alias_value":"5GKKQH3P35E4","created_at":"2026-07-05T01:17:06.404922+00:00"},{"alias_kind":"pith_short_16","alias_value":"5GKKQH3P35E4FDFD","created_at":"2026-07-05T01:17:06.404922+00:00"},{"alias_kind":"pith_short_8","alias_value":"5GKKQH3P","created_at":"2026-07-05T01:17:06.404922+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/5GKKQH3P35E4FDFDKTDE37H5PT","json":"https://pith.science/pith/5GKKQH3P35E4FDFDKTDE37H5PT.json","graph_json":"https://pith.science/api/pith-number/5GKKQH3P35E4FDFDKTDE37H5PT/graph.json","events_json":"https://pith.science/api/pith-number/5GKKQH3P35E4FDFDKTDE37H5PT/events.json","paper":"https://pith.science/paper/5GKKQH3P"},"agent_actions":{"view_html":"https://pith.science/pith/5GKKQH3P35E4FDFDKTDE37H5PT","download_json":"https://pith.science/pith/5GKKQH3P35E4FDFDKTDE37H5PT.json","view_paper":"https://pith.science/paper/5GKKQH3P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1908.09414&json=true","fetch_graph":"https://pith.science/api/pith-number/5GKKQH3P35E4FDFDKTDE37H5PT/graph.json","fetch_events":"https://pith.science/api/pith-number/5GKKQH3P35E4FDFDKTDE37H5PT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5GKKQH3P35E4FDFDKTDE37H5PT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5GKKQH3P35E4FDFDKTDE37H5PT/action/storage_attestation","attest_author":"https://pith.science/pith/5GKKQH3P35E4FDFDKTDE37H5PT/action/author_attestation","sign_citation":"https://pith.science/pith/5GKKQH3P35E4FDFDKTDE37H5PT/action/citation_signature","submit_replication":"https://pith.science/pith/5GKKQH3P35E4FDFDKTDE37H5PT/action/replication_record"}},"created_at":"2026-07-05T01:17:06.404922+00:00","updated_at":"2026-07-05T01:17:06.404922+00:00"}