{"id":"W2922871176","doi":"10.3934/ipi.2019023","title":"A variational gamma correction model for image contrast enhancement","year":2019,"lang":"en","type":"article","venue":"Inverse Problems and Imaging","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Pixel; Gamma correction; Regularization (linguistics); Contrast (vision); Computer science; Benchmark (surveying); Uniqueness; Energy functional; Artificial intelligence; Image (mathematics); Algorithm; Image quality; Function (biology); Mathematics; Computer vision","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":[0.000652025,0.0007650054,0.0006543947,0.000457378,0.0002761408,0.0006921636,0.00137286,0.001157309,0.001504143],"category_scores_gemma":[0.001115301,0.0004169243,0.0009923578,0.0003892957,0.0009534868,0.000965547,0.0008159085,0.001038943,0.0003412934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008644257,"about_ca_system_score_gemma":0.0007546872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004178694,"about_ca_topic_score_gemma":0.00312646,"domain_scores_codex":[0.9998123,0.00006014176,0.000006751761,0.00004120447,0.00005907477,0.00002041382],"domain_scores_gemma":[0.9998098,0.00009627912,0.00002047812,0.00001532622,0.00004423438,0.00001391273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004797682,0.00003035705,0.0003858156,0.0001161704,0.00005377907,0.0001476083,0.0001047839,0.8317623,0.02132774,0.1163567,0.001360575,0.02830626],"study_design_scores_gemma":[0.000003386769,0.00001225182,0.00005338143,0.000004068505,0.000005274238,0.00003800035,0.000004131097,0.9903494,0.0009580398,0.007708921,0.0008556703,0.000007494132],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003976706,0.000317235,0.9939005,0.0001370972,0.00002338389,0.00001806605,0.00002036687,0.00005287306,0.001553746],"genre_scores_gemma":[0.527526,0.001792651,0.4432336,0.0003778484,0.0001136829,0.0002505077,0.0002511763,0.0003410896,0.02611344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004178694,"threshold_uncertainty_score":0.008308768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01207813805170176,"score_gpt":0.2321716633949416,"score_spread":0.2200935253432398,"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."}}