{"id":"W4411282518","doi":"10.3390/electronics14122416","title":"Model-Based Design of Contrast-Limited Histogram Equalization for Low-Complexity, High-Speed, and Low-Power Tone-Mapping Operation","year":2025,"lang":"en","type":"article","venue":"Electronics","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Mitacs","keywords":"Tone (literature); Contrast (vision); Equalization (audio); Histogram equalization; Computer science; Tone mapping; Power (physics); Histogram; Adaptive histogram equalization; Electronic engineering; Speech recognition; Engineering; Artificial intelligence; Computer vision; Algorithm; Physics; Art; Image (mathematics); Dynamic range","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001541964,0.0003924731,0.0002703541,0.0001783944,0.0001985348,0.0006340678,0.000818981,0.0003085242,0.003136434],"category_scores_gemma":[0.0003365338,0.0001821416,0.0002908339,0.00009414105,0.0001985008,0.000403753,0.0002734329,0.0004326847,0.0006804574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005156563,"about_ca_system_score_gemma":0.0006909368,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001793795,"about_ca_topic_score_gemma":0.002763469,"domain_scores_codex":[0.9998753,0.00001577416,0.000005451773,0.00002844583,0.00005862511,0.00001633555],"domain_scores_gemma":[0.9998875,0.00002650509,0.00001737166,0.00001588363,0.00004701158,0.000005720987],"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":[0.0002734805,0.0001434505,0.001161445,0.000479606,0.00009281183,0.0002872253,0.0003323637,0.5843006,0.2355116,0.0287681,0.004096292,0.1445529],"study_design_scores_gemma":[0.00002435849,0.0001827917,0.0003142754,0.00002422455,0.00002999348,0.0001082552,0.00002275562,0.932438,0.05505252,0.001306469,0.01048332,0.00001311964],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0202616,0.0001805248,0.9669114,0.0001126183,0.00004817125,0.0001162933,0.00007870894,0.001238171,0.0110525],"genre_scores_gemma":[0.7924605,0.0003111918,0.1967266,0.000110677,0.00002212417,0.0002770528,0.0001417699,0.0001699906,0.009780131],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003136434,"threshold_uncertainty_score":0.01049238,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02881348737182138,"score_gpt":0.2941536552694838,"score_spread":0.2653401678976624,"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."}}