{"id":"W2153608978","doi":"10.1109/icassp.1982.1171589","title":"Noise reduction in images using statistical filtering","year":2005,"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 Windsor","funders":"","keywords":"Histogram; Computer science; Kalman filter; Noise (video); Reduction (mathematics); Noise reduction; Computer vision; Artificial intelligence; Computation; Median filter; Image (mathematics); Algorithm; Filter (signal processing); Image noise; Image processing; Mathematics","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":[0.0003285769,0.00006100135,0.00007850313,0.00009417384,0.00004211574,0.0001183698,0.0001830739,0.00002242398,0.00005224947],"category_scores_gemma":[0.00003697576,0.00005660169,0.00001527845,0.0001775326,0.0000204023,0.0005927529,0.00008050259,0.00007754095,0.00002900295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004787873,"about_ca_system_score_gemma":0.0000235859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007789912,"about_ca_topic_score_gemma":0.000003550408,"domain_scores_codex":[0.9993079,0.00007940688,0.0001419976,0.0001910011,0.0001088856,0.000170806],"domain_scores_gemma":[0.9997032,0.00004657087,0.00001877813,0.0001725897,0.00002152423,0.00003729725],"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":[0.00001274797,0.00006853815,0.00009515293,0.00001043058,0.000003535897,0.00005938263,0.0004912117,0.003815459,0.4835806,0.01599283,0.0008593158,0.4950108],"study_design_scores_gemma":[0.0005926585,0.00003837005,0.003465956,0.00003127835,0.000003732485,0.0001944032,0.0000242503,0.7292502,0.2587456,0.006185976,0.001192294,0.0002752783],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02383765,0.0000307231,0.9727969,0.0002960493,0.000136269,0.00003576548,3.846974e-7,0.00006216044,0.002804157],"genre_scores_gemma":[0.2710671,0.000002025898,0.7284737,0.00008079463,0.00007539933,8.514209e-7,2.743146e-7,0.000003067357,0.0002968589],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7254347,"threshold_uncertainty_score":0.230815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03594203443674251,"score_gpt":0.3258605315732309,"score_spread":0.2899184971364884,"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."}}