{"id":"W1899329334","doi":"10.1002/jmri.22003","title":"Adaptive non‐local means denoising of MR images with spatially varying noise levels","year":2009,"lang":"en","type":"article","venue":"Journal of Magnetic Resonance Imaging","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1133,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Noise (video); Computer science; Noise reduction; Filter (signal processing); Gaussian noise; Artificial intelligence; Bilateral filter; Rician fading; Median filter; Computer vision; Image noise; Sensitivity (control systems); Adaptive filter; Pattern recognition (psychology); Image processing; Image (mathematics); Algorithm","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":[0.001422787,0.0002786018,0.0005732422,0.0003481662,0.0001461592,0.0002266949,0.001073697,0.00004506085,0.00001683077],"category_scores_gemma":[0.0001431918,0.0002220718,0.0001729322,0.0005828674,0.000198437,0.00139144,0.0001039393,0.000449657,0.000002537799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008364374,"about_ca_system_score_gemma":0.0003451501,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003395163,"about_ca_topic_score_gemma":0.000001453252,"domain_scores_codex":[0.9971818,0.0002447512,0.0008495311,0.000338179,0.0009086406,0.0004770958],"domain_scores_gemma":[0.9976279,0.0002835581,0.0007336231,0.000441315,0.0007543967,0.0001592263],"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":[0.00022575,0.00009377929,0.00056288,0.00001589583,0.00001046928,0.0008633535,0.001098055,0.003174555,0.07234967,0.0001859285,0.0001671355,0.9212525],"study_design_scores_gemma":[0.01132513,0.006519522,0.198185,0.004864735,0.0002952385,0.00644162,0.0004344655,0.3954487,0.3588825,0.01382947,0.002004541,0.001769095],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01472338,0.008486887,0.973541,0.001140582,0.000188631,0.0001192718,0.000001882451,0.00002412044,0.001774218],"genre_scores_gemma":[0.5938495,0.00005031389,0.4055527,0.0003408586,0.0001113687,5.658654e-7,8.123493e-8,0.0000131061,0.00008150141],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9194834,"threshold_uncertainty_score":0.9055825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01618067395139978,"score_gpt":0.2612123504434333,"score_spread":0.2450316764920335,"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."}}