{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YTYFX2QCQIV3NGHJ7INOAW5VBD","short_pith_number":"pith:YTYFX2QC","schema_version":"1.0","canonical_sha256":"c4f05bea02822bb698e9fa1ae05bb508d362de2f1dc6c684d9bdbab3ef8faa9b","source":{"kind":"arxiv","id":"2606.06176","version":1},"attestation_state":"computed","paper":{"title":"RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Wang, Chih-Yung Wen, Haochen Hu, Yanrui Bin","submitted_at":"2026-06-04T13:48:56Z","abstract_excerpt":"Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced significantly, most rely on paired datasets with unstable label quality, which bottlenecks model performance. This paper proposes a diffusion-based, in-dataset self-supervised learning strategy designed to exploit the quality distribution of training labels. Specifically, we evaluate label quality via semantic perception embeddings from a pre-trained diffusion model in a training-free manner. These quality scores are subsequently quantized into noise"},"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":"2606.06176","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-06-04T13:48:56Z","cross_cats_sorted":[],"title_canon_sha256":"080873236984fc891277dcf195131bf54f4f98cc1937968d68b3a6776060165b","abstract_canon_sha256":"1085cf5b032670fb9e618c726054ac19e037177043749daf12e1ce66465229ed"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:15:36.313477Z","signature_b64":"xfbA6Ca7ghaIYVk5WOkfOr62zPcTWqLUkA33QG0Ozs4l1ZSiLrNS3qDdwtFZ0KPLjO9nXuNSdxviKLxdUgdmAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4f05bea02822bb698e9fa1ae05bb508d362de2f1dc6c684d9bdbab3ef8faa9b","last_reissued_at":"2026-06-05T01:15:36.313057Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:15:36.313057Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"RQUL-UIE: Revitalizing Quality-Unstable Labels for Underwater Image Enhancement via In-Dataset Self-Supervision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bing Wang, Chih-Yung Wen, Haochen Hu, Yanrui Bin","submitted_at":"2026-06-04T13:48:56Z","abstract_excerpt":"Underwater Image Enhancement (UIE) is essential for mitigating degradations caused by water medium. Although learning-based methods have advanced significantly, most rely on paired datasets with unstable label quality, which bottlenecks model performance. This paper proposes a diffusion-based, in-dataset self-supervised learning strategy designed to exploit the quality distribution of training labels. Specifically, we evaluate label quality via semantic perception embeddings from a pre-trained diffusion model in a training-free manner. These quality scores are subsequently quantized into noise"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.06176","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/2606.06176/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":"2606.06176","created_at":"2026-06-05T01:15:36.313119+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.06176v1","created_at":"2026-06-05T01:15:36.313119+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.06176","created_at":"2026-06-05T01:15:36.313119+00:00"},{"alias_kind":"pith_short_12","alias_value":"YTYFX2QCQIV3","created_at":"2026-06-05T01:15:36.313119+00:00"},{"alias_kind":"pith_short_16","alias_value":"YTYFX2QCQIV3NGHJ","created_at":"2026-06-05T01:15:36.313119+00:00"},{"alias_kind":"pith_short_8","alias_value":"YTYFX2QC","created_at":"2026-06-05T01:15:36.313119+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/YTYFX2QCQIV3NGHJ7INOAW5VBD","json":"https://pith.science/pith/YTYFX2QCQIV3NGHJ7INOAW5VBD.json","graph_json":"https://pith.science/api/pith-number/YTYFX2QCQIV3NGHJ7INOAW5VBD/graph.json","events_json":"https://pith.science/api/pith-number/YTYFX2QCQIV3NGHJ7INOAW5VBD/events.json","paper":"https://pith.science/paper/YTYFX2QC"},"agent_actions":{"view_html":"https://pith.science/pith/YTYFX2QCQIV3NGHJ7INOAW5VBD","download_json":"https://pith.science/pith/YTYFX2QCQIV3NGHJ7INOAW5VBD.json","view_paper":"https://pith.science/paper/YTYFX2QC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.06176&json=true","fetch_graph":"https://pith.science/api/pith-number/YTYFX2QCQIV3NGHJ7INOAW5VBD/graph.json","fetch_events":"https://pith.science/api/pith-number/YTYFX2QCQIV3NGHJ7INOAW5VBD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YTYFX2QCQIV3NGHJ7INOAW5VBD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YTYFX2QCQIV3NGHJ7INOAW5VBD/action/storage_attestation","attest_author":"https://pith.science/pith/YTYFX2QCQIV3NGHJ7INOAW5VBD/action/author_attestation","sign_citation":"https://pith.science/pith/YTYFX2QCQIV3NGHJ7INOAW5VBD/action/citation_signature","submit_replication":"https://pith.science/pith/YTYFX2QCQIV3NGHJ7INOAW5VBD/action/replication_record"}},"created_at":"2026-06-05T01:15:36.313119+00:00","updated_at":"2026-06-05T01:15:36.313119+00:00"}