{"id":"W2038096646","doi":"10.1016/j.sigpro.2011.03.021","title":"Stochastic image denoising based on Markov-chain Monte Carlo sampling","year":2011,"lang":"en","type":"article","venue":"Signal Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Markov chain Monte Carlo; Noise reduction; Monte Carlo method; Mathematics; Algorithm; Artificial intelligence; Markov chain; Nonparametric statistics; Pattern recognition (psychology); Computer science; Mathematical optimization; Statistics","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"],"consensus_categories":[],"category_scores_codex":[0.001298392,0.0003384176,0.0003163041,0.0003282157,0.0005421229,0.0005556747,0.0009194617,0.000106868,0.00004477245],"category_scores_gemma":[0.0001377295,0.0003165821,0.0001278372,0.0006240726,0.0001086234,0.0009696758,0.0001597247,0.0004030923,0.0000419057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009295731,"about_ca_system_score_gemma":0.0002433568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004738459,"about_ca_topic_score_gemma":0.00000241849,"domain_scores_codex":[0.9973988,0.0002371399,0.0004059148,0.000727073,0.0005688577,0.0006622013],"domain_scores_gemma":[0.9986252,0.0002687122,0.0002073457,0.0004822806,0.0002263454,0.0001900751],"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":[0.0003751213,0.0003222313,0.0001025633,0.0002646478,0.00003145485,0.0003928437,0.005430746,0.01634415,0.07171214,0.0007895356,0.0001445785,0.90409],"study_design_scores_gemma":[0.0008818271,0.0002302302,0.0004417664,0.0005616651,0.0000293912,0.00003756541,0.00008985739,0.9729146,0.01918389,0.004976898,0.0000383791,0.0006139811],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005713257,0.0002886718,0.9887819,0.0001023782,0.0001785269,0.000173236,0.000001564075,0.0003407729,0.004419736],"genre_scores_gemma":[0.633941,3.349225e-7,0.3652385,0.0005901566,0.0001018053,0.000009032837,6.23722e-7,0.00003020397,0.00008835886],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9565704,"threshold_uncertainty_score":0.9999287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04856850487225214,"score_gpt":0.2870733484333325,"score_spread":0.2385048435610804,"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."}}