{"id":"W3203878315","doi":"10.18280/ts.380436","title":"Impulse Noise Removal Based on Hybrid Genetic Algorithm","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Impulse noise; Peak signal-to-noise ratio; Crossover; Salt-and-pepper noise; Algorithm; Noise (video); Genetic algorithm; Population; Computer science; Median filter; Impulse (physics); Noise reduction; Artificial intelligence; Mathematics; Pattern recognition (psychology); Image (mathematics); Image processing; Mathematical optimization; Pixel","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":[0.0005945998,0.0005751421,0.0007770319,0.001158284,0.000398765,0.0007869131,0.001063664,0.0008655867,0.0008524728],"category_scores_gemma":[0.001157891,0.000237532,0.0007985504,0.0007227582,0.0005008479,0.0006946335,0.000517434,0.000534255,0.0002708722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004900549,"about_ca_system_score_gemma":0.0005702051,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002668717,"about_ca_topic_score_gemma":0.001810615,"domain_scores_codex":[0.9994925,0.00008620427,0.00002118614,0.0001033758,0.0002455003,0.00005119358],"domain_scores_gemma":[0.9996942,0.0001294744,0.00003419883,0.00003134639,0.00009507686,0.00001563661],"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":[0.0001365058,0.0001390095,0.00265279,0.0001565517,0.0002286869,0.0002780388,0.0002939743,0.5753672,0.04921308,0.01497279,0.001172313,0.3553891],"study_design_scores_gemma":[0.00002469817,0.000102386,0.0007026822,0.0000173018,0.0000500838,0.0002152512,0.0000334295,0.9822482,0.009877687,0.003681904,0.003019777,0.00002664018],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02668608,0.000317021,0.9699591,0.0000722342,0.00004907199,0.00004581202,0.00001425318,0.0005758148,0.002280599],"genre_scores_gemma":[0.4210645,0.0006030428,0.5719509,0.0001973793,0.00005863077,0.0002374711,0.0001404123,0.0001326378,0.005615075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002668717,"threshold_uncertainty_score":0.005306423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01553031289705666,"score_gpt":0.252025692045934,"score_spread":0.2364953791488774,"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."}}