{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:IJ4SFNBZTTPKE3AVVNNXK5Q5KJ","short_pith_number":"pith:IJ4SFNBZ","schema_version":"1.0","canonical_sha256":"427922b4399cdea26c15ab5b75761d525f9f85e3ab166bd32c27701978d3648b","source":{"kind":"arxiv","id":"2509.15814","version":1},"attestation_state":"computed","paper":{"title":"QWD-GAN: Quality-aware Wavelet-driven GAN for Unsupervised Medical Microscopy Images Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hujun Yin, Lintao Xiang, Qijun Yang, Yating Huang","submitted_at":"2025-09-19T09:41:48Z","abstract_excerpt":"Image denoising plays a critical role in biomedical and microscopy imaging, especially when acquiring wide-field fluorescence-stained images. This task faces challenges in multiple fronts, including limitations in image acquisition conditions, complex noise types, algorithm adaptability, and clinical application demands. Although many deep learning-based denoising techniques have demonstrated promising results, further improvements are needed in preserving image details, enhancing algorithmic efficiency, and increasing clinical interpretability. We propose an unsupervised image denoising metho"},"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":"2509.15814","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-09-19T09:41:48Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"cf590305cd2a4f656b12297fd468f98f68cc785ab7f8aee9d8c55d87b8f23c4d","abstract_canon_sha256":"f84663bdeda68021316ef8fade7bdb0e6df4d31e3e4352ced87dfea3343455ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-26T01:03:15.768764Z","signature_b64":"Y1blTDP3jaqSnU1h+Rv8oyFdD52mSOWOT86vsJ/9qS9Jj5SBBtMd9jViT/2meoKJ/g4qwnget9aL0mn1AyFFDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"427922b4399cdea26c15ab5b75761d525f9f85e3ab166bd32c27701978d3648b","last_reissued_at":"2026-05-26T01:03:15.768167Z","signature_status":"signed_v1","first_computed_at":"2026-05-26T01:03:15.768167Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"QWD-GAN: Quality-aware Wavelet-driven GAN for Unsupervised Medical Microscopy Images Denoising","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Hujun Yin, Lintao Xiang, Qijun Yang, Yating Huang","submitted_at":"2025-09-19T09:41:48Z","abstract_excerpt":"Image denoising plays a critical role in biomedical and microscopy imaging, especially when acquiring wide-field fluorescence-stained images. This task faces challenges in multiple fronts, including limitations in image acquisition conditions, complex noise types, algorithm adaptability, and clinical application demands. Although many deep learning-based denoising techniques have demonstrated promising results, further improvements are needed in preserving image details, enhancing algorithmic efficiency, and increasing clinical interpretability. We propose an unsupervised image denoising metho"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.15814","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/2509.15814/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":"2509.15814","created_at":"2026-05-26T01:03:15.768249+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.15814v1","created_at":"2026-05-26T01:03:15.768249+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.15814","created_at":"2026-05-26T01:03:15.768249+00:00"},{"alias_kind":"pith_short_12","alias_value":"IJ4SFNBZTTPK","created_at":"2026-05-26T01:03:15.768249+00:00"},{"alias_kind":"pith_short_16","alias_value":"IJ4SFNBZTTPKE3AV","created_at":"2026-05-26T01:03:15.768249+00:00"},{"alias_kind":"pith_short_8","alias_value":"IJ4SFNBZ","created_at":"2026-05-26T01:03:15.768249+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/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ","json":"https://pith.science/pith/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ.json","graph_json":"https://pith.science/api/pith-number/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ/graph.json","events_json":"https://pith.science/api/pith-number/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ/events.json","paper":"https://pith.science/paper/IJ4SFNBZ"},"agent_actions":{"view_html":"https://pith.science/pith/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ","download_json":"https://pith.science/pith/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ.json","view_paper":"https://pith.science/paper/IJ4SFNBZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.15814&json=true","fetch_graph":"https://pith.science/api/pith-number/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ/graph.json","fetch_events":"https://pith.science/api/pith-number/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ/action/storage_attestation","attest_author":"https://pith.science/pith/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ/action/author_attestation","sign_citation":"https://pith.science/pith/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ/action/citation_signature","submit_replication":"https://pith.science/pith/IJ4SFNBZTTPKE3AVVNNXK5Q5KJ/action/replication_record"}},"created_at":"2026-05-26T01:03:15.768249+00:00","updated_at":"2026-05-26T01:03:15.768249+00:00"}