{"id":"W1849986882","doi":"10.1109/iscas.2004.1328896","title":"Color image filtering and enhancement based genetic algorithms","year":2004,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Bell (Canada)","funders":"","keywords":"Computer science; Computer vision; Artificial intelligence; Color image; Color filter array; Demosaicing; Genetic algorithm; Image restoration; Image (mathematics); Filter (signal processing); Image enhancement; Noise (video); Color correction; Algorithm; Color gel; Image processing","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":[0.0007069241,0.0006019217,0.0005274935,0.0007323913,0.0002786464,0.0007517486,0.0006549475,0.0009535276,0.001027574],"category_scores_gemma":[0.001501374,0.0002474998,0.000559044,0.0006702218,0.0005955498,0.0004322617,0.0003328066,0.0005616067,0.0002877281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005676432,"about_ca_system_score_gemma":0.000587732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003000615,"about_ca_topic_score_gemma":0.002883898,"domain_scores_codex":[0.9996886,0.00008759326,0.00001329182,0.00006272127,0.0001151056,0.00003260336],"domain_scores_gemma":[0.9996325,0.000167818,0.00004293419,0.00003292071,0.0001125025,0.00001143385],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007702897,0.00009731925,0.0007757225,0.00007430261,0.00007939785,0.0000736657,0.00008778313,0.7270632,0.02172123,0.02744064,0.001064432,0.2214452],"study_design_scores_gemma":[0.00001705866,0.00004081839,0.0001763091,0.00000854253,0.00001806988,0.00003215016,0.00000698876,0.9914677,0.003982013,0.002864156,0.001379225,0.000007101146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01472811,0.0001991511,0.981849,0.00007345731,0.00003631874,0.00004375415,0.00001264118,0.0003344278,0.002723234],"genre_scores_gemma":[0.3028506,0.0005657763,0.6904402,0.0001779644,0.00004841116,0.0002321349,0.00008137825,0.00008203262,0.005521509],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003000615,"threshold_uncertainty_score":0.005966306,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009457314258301179,"score_gpt":0.243649193872918,"score_spread":0.2341918796146168,"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."}}