{"id":"W3083374363","doi":"10.1109/mwscas48704.2020.9184525","title":"Image Segmentation and Adaptive Contrast Enhancement for Haze Removal","year":2020,"lang":"en","type":"article","venue":"","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Haze; Artificial intelligence; Adaptive histogram equalization; Computer science; Computer vision; Noise (video); Image restoration; Bilateral filter; Image segmentation; Pixel; Contrast (vision); Image (mathematics); Filter (signal processing); Noise reduction; Pattern recognition (psychology); Histogram; Image processing; Physics; Histogram equalization","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":[0.0001176972,0.00009497719,0.0001005272,0.00002262541,0.00006105886,0.0001157275,0.0002071151,0.0000196026,0.0000295353],"category_scores_gemma":[0.00002394239,0.00008778227,0.00002406227,0.00008861852,0.00003059418,0.0005862571,0.0001181037,0.00003667777,0.00001428317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002653209,"about_ca_system_score_gemma":0.00001983108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000540358,"about_ca_topic_score_gemma":0.000001453996,"domain_scores_codex":[0.999237,0.00001857313,0.0001465343,0.0003031858,0.0001341449,0.0001605488],"domain_scores_gemma":[0.9996545,0.00003172209,0.00005333033,0.0001233964,0.00007373903,0.0000632743],"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":[0.00004122877,0.00003656413,0.000005913619,0.00002613277,0.00002063334,0.000008408333,0.001065098,3.771536e-7,0.8421738,0.03389745,0.0172571,0.1054673],"study_design_scores_gemma":[0.0005420003,0.0004802124,0.00002665063,0.000008134481,0.000005822213,0.000003385674,0.00008546394,0.1007781,0.8934683,0.00129723,0.003156879,0.0001478086],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00106528,0.00004140001,0.9901395,0.004453394,0.00005212119,0.0006178968,0.000002455198,0.0002587438,0.003369239],"genre_scores_gemma":[0.1144557,0.00001336388,0.8827451,0.002316839,0.00004336028,0.00009079093,0.000003375571,0.000005874479,0.00032556],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1133904,"threshold_uncertainty_score":0.3579657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02284100806232699,"score_gpt":0.2679188595837792,"score_spread":0.2450778515214522,"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."}}