{"id":"W4409981422","doi":"10.18280/ts.420202","title":"An Automated Contrast Enhancement Approach for Aerial Images","year":2025,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Princess Nourah Bint Abdulrahman University","keywords":"Contrast (vision); Contrast enhancement; Artificial intelligence; Computer science; Computer vision; Aerial photos; Remote sensing; Pattern recognition (psychology); Geology; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002695154,0.0005553931,0.0003474677,0.000980998,0.0002363295,0.0005738316,0.000550621,0.0004994901,0.001537134],"category_scores_gemma":[0.0006757353,0.0002404601,0.0005617515,0.0004567883,0.0003041243,0.0006132731,0.0005285204,0.0005845254,0.0006338871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002551528,"about_ca_system_score_gemma":0.0004032574,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007564094,"about_ca_topic_score_gemma":0.001260534,"domain_scores_codex":[0.9997721,0.00002956332,0.00001099199,0.0000551476,0.0001086123,0.00002368275],"domain_scores_gemma":[0.9997645,0.00005394059,0.00003894692,0.00003806116,0.00009358799,0.00001093143],"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":[0.00012585,0.00006261943,0.0005903985,0.0002523137,0.00005728964,0.0002339029,0.0001425355,0.02438851,0.4844009,0.004839063,0.001739516,0.4831671],"study_design_scores_gemma":[0.00003613757,0.0003997156,0.003525984,0.00005246334,0.00009044389,0.001905163,0.0001007696,0.6478286,0.3190005,0.004337328,0.02265744,0.00006555624],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01407077,0.0003010226,0.9833142,0.00006892747,0.00003535692,0.00006816701,0.00002864053,0.0004278125,0.001685197],"genre_scores_gemma":[0.1316526,0.0005031002,0.8638848,0.00007938208,0.00005261477,0.00007229193,0.0001266163,0.00008296915,0.003545626],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001537134,"threshold_uncertainty_score":0.005142272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01321386416395457,"score_gpt":0.3068677439834607,"score_spread":0.2936538798195061,"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."}}