{"id":"W4285218761","doi":"10.1109/access.2022.3178745","title":"Learning Tone Curves for Local Image Enhancement","year":2022,"lang":"en","type":"article","venue":"IEEE Access","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre for Social Innovation","funders":"","keywords":"Computer science; Tone mapping; Tone (literature); Artificial intelligence; Software; Computer vision; Pixel; Image (mathematics); Interpretability","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005295668,0.0001255106,0.0001463384,0.00009106964,0.000375035,0.0001999672,0.001854454,0.00001584018,0.0001984117],"category_scores_gemma":[0.00002697515,0.0001356588,0.00006259401,0.0003355519,0.00004115585,0.001089824,0.0008548495,0.0001989745,0.00001576348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001263402,"about_ca_system_score_gemma":0.00005587173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003719006,"about_ca_topic_score_gemma":0.000002791525,"domain_scores_codex":[0.9985936,0.00006884242,0.0002150217,0.000400546,0.0003913808,0.0003305664],"domain_scores_gemma":[0.9992726,0.00007642444,0.0001194292,0.0004038497,0.00008511711,0.00004255017],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005660874,0.0009010439,0.0002234062,0.0005542358,0.0000888299,0.00007725554,0.001464575,0.001802054,0.2076931,0.006305724,0.3056166,0.4752166],"study_design_scores_gemma":[0.0003735228,0.0005058984,0.00005460947,0.000046397,0.000009006465,0.000007603769,0.00004711929,0.03610573,0.9083779,0.001566055,0.0525658,0.0003402987],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002627502,0.0002332104,0.9928172,0.0009970685,0.0005849747,0.0005195795,0.000002884461,0.000430727,0.001786805],"genre_scores_gemma":[0.9170586,0.0001074704,0.07560588,0.002470991,0.0001144182,0.001796495,0.00001621291,0.00002699637,0.002802922],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9172114,"threshold_uncertainty_score":0.5532005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02342674653744157,"score_gpt":0.3463302204229295,"score_spread":0.3229034738854879,"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."}}