{"id":"W2602101963","doi":"10.1016/j.isprsjprs.2017.02.016","title":"Enhancement of low visibility aerial images using histogram truncation and an explicit Retinex representation for balancing contrast and color consistency","year":2017,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Alberta Innovates - Technology Futures","keywords":"Color constancy; Visibility; Contrast (vision); Artificial intelligence; Computer vision; Computer science; Histogram; Consistency (knowledge bases); Generality; Representation (politics); Scale (ratio); Truncation (statistics); High dynamic range; Mathematics; Image (mathematics); Dynamic range; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001304632,0.0001508426,0.0003762905,0.0001511574,0.0004088666,0.0003383352,0.0001701534,0.00007312288,5.937732e-7],"category_scores_gemma":[0.0005029077,0.000137971,0.00006223813,0.00007965536,0.0002406315,0.0008557102,0.0001021726,0.0001200709,1.727311e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004956119,"about_ca_system_score_gemma":0.00005691009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004834097,"about_ca_topic_score_gemma":0.00003526784,"domain_scores_codex":[0.9985843,0.000113452,0.0005819615,0.0003038883,0.0002160648,0.0002003359],"domain_scores_gemma":[0.9978231,0.0001550649,0.001105572,0.0003586449,0.0004544024,0.0001032671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000126849,0.00002967674,0.0002297638,0.00008667399,0.00002067844,0.000006652236,0.0004238418,0.000001008795,0.6760461,0.00003882343,0.000006911536,0.322983],"study_design_scores_gemma":[0.001396236,0.0006308501,0.002156393,0.0004047937,0.00006034257,0.000215182,0.0002405398,0.1570778,0.8356835,0.001912266,0.000033666,0.0001885076],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.483592,0.0001470946,0.5157688,0.00006396691,0.000177006,0.0002210677,0.00000105321,0.00000994982,0.00001900971],"genre_scores_gemma":[0.6707236,0.0001050462,0.3290484,0.00002852205,0.00008409881,1.26041e-7,8.088082e-7,0.000006219738,0.000003114953],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3227945,"threshold_uncertainty_score":0.5626292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02602166899504073,"score_gpt":0.3312813776359922,"score_spread":0.3052597086409515,"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."}}