{"id":"W1958496189","doi":"10.1109/icassp.1989.266708","title":"Nonlinear adaptive restoration of images with multiplicative noise","year":2003,"lang":"en","type":"article","venue":"International Conference on Acoustics, Speech, and Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Multiplicative noise; Multiplicative function; Pointwise; Mathematics; Nonlinear system; Preprocessor; Algorithm; Normalization (sociology); Image restoration; Computer science; Image processing; Artificial intelligence; Image (mathematics); Mathematical analysis","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":[0.000390969,0.0003971813,0.0003610312,0.0004343004,0.0001941489,0.0005899337,0.0004553382,0.0004807051,0.00109105],"category_scores_gemma":[0.001443719,0.0002168664,0.0003617959,0.0004125135,0.0005966776,0.0006821807,0.0006680822,0.0005205775,0.0004162682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002981596,"about_ca_system_score_gemma":0.000297278,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007749714,"about_ca_topic_score_gemma":0.001129463,"domain_scores_codex":[0.9997447,0.0000397343,0.00001100384,0.00005444471,0.000135756,0.00001444877],"domain_scores_gemma":[0.9997413,0.00008135134,0.00003995817,0.00004865449,0.00007895482,0.000009904348],"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":[0.0003046779,0.00008426305,0.0009995741,0.0003363729,0.0000766918,0.000302817,0.0003178318,0.2345742,0.3479049,0.06723545,0.003492911,0.3443705],"study_design_scores_gemma":[0.000008399893,0.00006071669,0.0005794677,0.00001411838,0.00001670977,0.0002514811,0.00003336294,0.9283026,0.05332119,0.01280535,0.004586851,0.00001965054],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03338047,0.0003920987,0.9627451,0.0001733429,0.00007561718,0.00001784679,0.0000252289,0.0002576151,0.002932664],"genre_scores_gemma":[0.4501709,0.00137495,0.5328907,0.0001588416,0.0001620136,0.0000673412,0.0001388361,0.0001508169,0.01488562],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00109105,"threshold_uncertainty_score":0.00364995,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04132328173896809,"score_gpt":0.3061722528023765,"score_spread":0.2648489710634084,"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."}}