{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:QGKFGJVXRQUQZDFVCJJ7SQP35L","short_pith_number":"pith:QGKFGJVX","schema_version":"1.0","canonical_sha256":"81945326b78c290c8cb51253f941fbeae36c4d262245c4f6a625b27e64080322","source":{"kind":"arxiv","id":"2002.05333","version":1},"attestation_state":"computed","paper":{"title":"MLFcGAN: Multi-level Feature Fusion based Conditional GAN for Underwater Image Color Correction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Ben M. Chen, XiaoDong Liu, Zhi Gao","submitted_at":"2020-02-13T04:15:10Z","abstract_excerpt":"Color correction for underwater images has received increasing interests, due to its critical role in facilitating available mature vision algorithms for underwater scenarios. Inspired by the stunning success of deep convolutional neural networks (DCNNs) techniques in many vision tasks, especially the strength in extracting features in multiple scales, we propose a deep multi-scale feature fusion net based on the conditional generative adversarial network (GAN) for underwater image color correction. In our network, multi-scale features are extracted first, followed by augmenting local features"},"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":"2002.05333","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-02-13T04:15:10Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"e66ebd7e73bdac29d534ce8e0585c34a1260d279c029d688e47f6a1cd04f44d7","abstract_canon_sha256":"c81642f08853db9d6861802d36d089b9e23370f328567834c0b88958bae05762"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:40:30.248587Z","signature_b64":"hmOFacpRyG9zWi7VIx3XYeLpenPdM1cPuwy5na9oXhuOYylERLtKyt6W+8fArhsP+reAZa6xekILIq0zvH5gDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"81945326b78c290c8cb51253f941fbeae36c4d262245c4f6a625b27e64080322","last_reissued_at":"2026-07-05T00:40:30.248175Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:40:30.248175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MLFcGAN: Multi-level Feature Fusion based Conditional GAN for Underwater Image Color Correction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Ben M. Chen, XiaoDong Liu, Zhi Gao","submitted_at":"2020-02-13T04:15:10Z","abstract_excerpt":"Color correction for underwater images has received increasing interests, due to its critical role in facilitating available mature vision algorithms for underwater scenarios. Inspired by the stunning success of deep convolutional neural networks (DCNNs) techniques in many vision tasks, especially the strength in extracting features in multiple scales, we propose a deep multi-scale feature fusion net based on the conditional generative adversarial network (GAN) for underwater image color correction. In our network, multi-scale features are extracted first, followed by augmenting local features"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2002.05333","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/2002.05333/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":"2002.05333","created_at":"2026-07-05T00:40:30.248234+00:00"},{"alias_kind":"arxiv_version","alias_value":"2002.05333v1","created_at":"2026-07-05T00:40:30.248234+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2002.05333","created_at":"2026-07-05T00:40:30.248234+00:00"},{"alias_kind":"pith_short_12","alias_value":"QGKFGJVXRQUQ","created_at":"2026-07-05T00:40:30.248234+00:00"},{"alias_kind":"pith_short_16","alias_value":"QGKFGJVXRQUQZDFV","created_at":"2026-07-05T00:40:30.248234+00:00"},{"alias_kind":"pith_short_8","alias_value":"QGKFGJVX","created_at":"2026-07-05T00:40:30.248234+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/QGKFGJVXRQUQZDFVCJJ7SQP35L","json":"https://pith.science/pith/QGKFGJVXRQUQZDFVCJJ7SQP35L.json","graph_json":"https://pith.science/api/pith-number/QGKFGJVXRQUQZDFVCJJ7SQP35L/graph.json","events_json":"https://pith.science/api/pith-number/QGKFGJVXRQUQZDFVCJJ7SQP35L/events.json","paper":"https://pith.science/paper/QGKFGJVX"},"agent_actions":{"view_html":"https://pith.science/pith/QGKFGJVXRQUQZDFVCJJ7SQP35L","download_json":"https://pith.science/pith/QGKFGJVXRQUQZDFVCJJ7SQP35L.json","view_paper":"https://pith.science/paper/QGKFGJVX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2002.05333&json=true","fetch_graph":"https://pith.science/api/pith-number/QGKFGJVXRQUQZDFVCJJ7SQP35L/graph.json","fetch_events":"https://pith.science/api/pith-number/QGKFGJVXRQUQZDFVCJJ7SQP35L/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QGKFGJVXRQUQZDFVCJJ7SQP35L/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QGKFGJVXRQUQZDFVCJJ7SQP35L/action/storage_attestation","attest_author":"https://pith.science/pith/QGKFGJVXRQUQZDFVCJJ7SQP35L/action/author_attestation","sign_citation":"https://pith.science/pith/QGKFGJVXRQUQZDFVCJJ7SQP35L/action/citation_signature","submit_replication":"https://pith.science/pith/QGKFGJVXRQUQZDFVCJJ7SQP35L/action/replication_record"}},"created_at":"2026-07-05T00:40:30.248234+00:00","updated_at":"2026-07-05T00:40:30.248234+00:00"}