{"id":"W2159432967","doi":"10.1109/ccece.1997.608332","title":"Adaptive multichannel filter for image processing","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Gaussian noise; Value noise; Noise (video); Image noise; Gradient noise; Computer science; Median filter; Noise measurement; Probability density function; Filter (signal processing); Artificial intelligence; Salt-and-pepper noise; Adaptive filter; Algorithm; Image (mathematics); Computer vision; Pattern recognition (psychology); Mathematics; Image processing; Noise reduction; Statistics","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":[0.0003423657,0.0004510943,0.0004522339,0.0004678371,0.0002476991,0.0004498346,0.0005491622,0.0009458061,0.003004247],"category_scores_gemma":[0.0006254221,0.0001235055,0.0004241586,0.0006604173,0.0003024845,0.0006147875,0.0003582774,0.0007295724,0.001344988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003963401,"about_ca_system_score_gemma":0.0004553562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001096067,"about_ca_topic_score_gemma":0.001723261,"domain_scores_codex":[0.999702,0.000048082,0.00001215419,0.00006590608,0.000152508,0.00001920773],"domain_scores_gemma":[0.9998074,0.00004902773,0.0000173142,0.00002541031,0.00009237424,0.000008468338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001503556,0.00005613799,0.000278656,0.0002800074,0.00007369706,0.0001141743,0.00006631474,0.02645387,0.1012399,0.03666471,0.008460315,0.8261619],"study_design_scores_gemma":[0.00002948856,0.000199693,0.001009074,0.00004875046,0.00005743047,0.0005272056,0.00002409664,0.8368974,0.05203801,0.01809928,0.0910084,0.00006122719],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002787291,0.001462967,0.9934058,0.0001431328,0.0001591704,0.00002295047,0.00002923717,0.0003075258,0.001681843],"genre_scores_gemma":[0.07513942,0.002524429,0.9073633,0.0003435694,0.0002765751,0.0001562497,0.0001731658,0.00006697766,0.0139564],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003004247,"threshold_uncertainty_score":0.01005024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06568139152361831,"score_gpt":0.2958583258254752,"score_spread":0.2301769343018569,"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."}}