{"id":"W3035082476","doi":"10.1109/cvprw50498.2020.00277","title":"L<sup>2</sup>UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion","year":2020,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Contrast (vision); Underwater; Artificial intelligence; Luminance; Computer vision; Scale (ratio); Computer science; Fusion; Image fusion; Image (mathematics); Physics; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007173667,0.0007115785,0.0004816254,0.0006594238,0.0002192053,0.0007522316,0.001169134,0.0006541951,0.001843336],"category_scores_gemma":[0.001018893,0.0003209032,0.0007870724,0.0003320425,0.0005729402,0.001097029,0.001084086,0.0008627607,0.0006949102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003539135,"about_ca_system_score_gemma":0.0003317043,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001587579,"about_ca_topic_score_gemma":0.002366609,"domain_scores_codex":[0.999804,0.00003383504,0.000009959015,0.00003797064,0.00009045335,0.0000237743],"domain_scores_gemma":[0.9996916,0.00009474768,0.0000434352,0.00006940091,0.00007428608,0.00002666961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002726758,0.0001655053,0.0007699323,0.0003764745,0.0001182867,0.0003052633,0.0002266729,0.1394201,0.4018362,0.02579119,0.004478988,0.4262387],"study_design_scores_gemma":[0.00001745538,0.0001389578,0.0007233358,0.00001954167,0.00003820401,0.0003331321,0.00003171485,0.8510062,0.1338548,0.004474715,0.009320753,0.00004120647],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006559219,0.0001400063,0.9912981,0.00003821437,0.00001449862,0.0000359336,0.00002984981,0.001088839,0.0007953139],"genre_scores_gemma":[0.1276732,0.000301251,0.8685203,0.00007492483,0.0000292208,0.0000749572,0.0001389255,0.000431802,0.002755429],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001843336,"threshold_uncertainty_score":0.006166577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02114234276352955,"score_gpt":0.2724236443399395,"score_spread":0.2512813015764099,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}