{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002906573,0.0001188628,0.0001138567,0.00008216049,0.00009469027,0.0002085871,0.0001821396,0.00002076479,0.0000253772],"category_scores_gemma":[0.00001434966,0.0001189923,0.00003577011,0.00008424706,0.00002798409,0.001184412,0.000115209,0.00007179139,0.00002354429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005823914,"about_ca_system_score_gemma":0.00004480238,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002537448,"about_ca_topic_score_gemma":0.000003376902,"domain_scores_codex":[0.9990666,0.00001339102,0.000185476,0.0003463998,0.0001504803,0.0002376696],"domain_scores_gemma":[0.9995031,0.00003394728,0.00009583476,0.0001982025,0.0001269827,0.00004193047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003808292,0.0002309197,0.001620105,0.0002842055,0.00005913932,0.000003938894,0.003867543,0.001657826,0.8179285,0.0470571,0.03153668,0.09571595],"study_design_scores_gemma":[0.0005005344,0.00003583162,0.00004029692,0.00004437669,0.000004358253,0.000006660016,0.00001363888,0.9672005,0.02257348,0.007323594,0.002106215,0.0001505302],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002621024,0.00003872419,0.9935027,0.0006533231,0.0004153631,0.0006573452,0.00000277587,0.0001659371,0.001942815],"genre_scores_gemma":[0.4842955,0.00002531763,0.5106173,0.001025308,0.00004735681,0.0002181492,0.000009173865,0.00001490712,0.003747005],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9655427,"threshold_uncertainty_score":0.4852365,"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."}}