{"id":"W2118071128","doi":"10.1109/tsp.2008.2011832","title":"Robust NL-Means Filter With Optimal Pixel-Wise Smoothing Parameter for Statistical Image Denoising","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Smoothing; Noise reduction; Pixel; Weighting; Filter (signal processing); Computer science; Non-local means; Artificial intelligence; Mathematics; Multiplicative noise; Statistic; Algorithm; Pattern recognition (psychology); Statistics; Computer vision; Image denoising","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":[0.0006277401,0.0003842377,0.0003777197,0.000270894,0.0008549652,0.001130506,0.0005527977,0.0001279056,0.00003252145],"category_scores_gemma":[0.00001902109,0.0003290722,0.000141605,0.000510181,0.0001346831,0.001774519,0.000003213947,0.0005197008,0.00001495041],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001013335,"about_ca_system_score_gemma":0.0002041142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006115441,"about_ca_topic_score_gemma":0.000002236462,"domain_scores_codex":[0.9973804,0.0001538353,0.000445515,0.0007934377,0.0005277822,0.0006990249],"domain_scores_gemma":[0.9983862,0.00061962,0.000142495,0.0003654812,0.0002752152,0.0002109664],"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":[0.0006421318,0.0004233727,0.000001697951,0.0001154715,0.00005038881,0.0001191397,0.001553604,0.09865435,0.04568829,0.0003251699,0.0001634769,0.8522629],"study_design_scores_gemma":[0.002490256,0.001614839,0.00005149866,0.0004691288,0.0001698043,0.0002318723,0.0001183795,0.8485812,0.1417127,0.003219905,0.000346209,0.0009942326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002225289,0.00006348034,0.996124,0.0004535462,0.0001473426,0.0003424694,0.00001657368,0.0003021523,0.0003251244],"genre_scores_gemma":[0.4397042,0.000001674625,0.5594429,0.0006207784,0.000068433,0.00002196327,0.000001933046,0.00002613147,0.0001119677],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8512686,"threshold_uncertainty_score":0.9999161,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03756351195907125,"score_gpt":0.2892417891859363,"score_spread":0.2516782772268651,"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."}}