{"id":"W1992201949","doi":"10.1109/icip.2011.6116065","title":"SSIM-based non-local means image denoising","year":2011,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":59,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Pattern recognition (psychology); Image quality; Computer science; Noise reduction; Similarity (geometry); Image (mathematics); Computer vision; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006283789,0.0001512871,0.0001618375,0.0001206551,0.0001441574,0.0001660027,0.0008565225,0.00006032461,0.0001791917],"category_scores_gemma":[0.00003671412,0.0001256667,0.00009599388,0.000335567,0.00009580182,0.0005991176,0.0001467111,0.0001365133,0.0002979965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003077117,"about_ca_system_score_gemma":0.00009813242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001704525,"about_ca_topic_score_gemma":0.000008124616,"domain_scores_codex":[0.9986861,0.0001231245,0.0002066414,0.0003716878,0.0002518275,0.0003605731],"domain_scores_gemma":[0.9990097,0.0000950153,0.00004852698,0.0006164105,0.0001020903,0.0001282403],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000125307,0.0006511394,0.0006587149,0.00008133122,0.00007207911,0.001267474,0.006176631,0.0002214448,0.1562779,0.05419281,0.0116787,0.7685965],"study_design_scores_gemma":[0.001110897,0.0001843417,0.002501409,0.00003697585,0.00001601842,0.00003439121,0.00006139881,0.3117258,0.6728856,0.009562028,0.001385657,0.0004954928],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001462525,0.00002006187,0.9168724,0.0001553692,0.0002537188,0.00007036273,3.054611e-7,0.0002329393,0.08093234],"genre_scores_gemma":[0.308816,5.535302e-7,0.6892067,0.001178901,0.00003567394,0.000003035193,4.365732e-7,0.00001031934,0.000748462],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.768101,"threshold_uncertainty_score":0.5124539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03457591210876104,"score_gpt":0.2701938598109336,"score_spread":0.2356179477021726,"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."}}