{"id":"W2093473251","doi":"10.1049/ip-vis:20020626","title":"Nonlinear filtering for phase image denoising","year":2002,"lang":"en","type":"article","venue":"IEE Proceedings - Vision Image and Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Noise reduction; Noise (video); Filter (signal processing); Image (mathematics); Phase (matter); Nonlinear system; Artificial intelligence; Algorithm; Computer science; Median filter; Monte Carlo method; Computer vision; Pattern recognition (psychology); Mathematics; Image processing; Statistics; Physics","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.001042209,0.000425726,0.0004558253,0.0003351088,0.001027348,0.003112928,0.0007152864,0.0001413472,0.00005005789],"category_scores_gemma":[0.0002659353,0.0003938149,0.0001456326,0.0006152428,0.000176622,0.005077522,0.0003057087,0.0003320694,0.0000255294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004547698,"about_ca_system_score_gemma":0.00004823498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003632206,"about_ca_topic_score_gemma":1.119913e-7,"domain_scores_codex":[0.9971322,0.00002924206,0.0006066201,0.0009836792,0.0004886463,0.0007596373],"domain_scores_gemma":[0.9983385,0.0001621166,0.0002993311,0.000189015,0.000665916,0.0003451546],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004680596,0.0001337959,0.000003992057,0.0002805978,0.000008272751,0.00002454487,0.001162231,7.405993e-7,0.513862,0.00008928158,0.001484585,0.4829031],"study_design_scores_gemma":[0.003741033,0.0007395265,0.00001045611,0.0005541173,0.00004986191,0.0002629593,0.0001835987,0.6929664,0.2901165,0.003684773,0.006909459,0.0007813557],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01906389,0.001417995,0.9746259,0.00101717,0.0001165832,0.0003993323,0.000005913972,0.0003874597,0.002965728],"genre_scores_gemma":[0.2385444,0.00006334823,0.7593356,0.0008830244,0.0004918744,0.00004230861,0.000003450491,0.0000684683,0.0005674794],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6929657,"threshold_uncertainty_score":0.9998513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03042526565361427,"score_gpt":0.3227734435597693,"score_spread":0.292348177906155,"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."}}