{"id":"W2133226466","doi":"10.1109/icip.2008.4711810","title":"A perceptually adaptive approach to image denoising using anisotropic non-local means","year":2008,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Non-local means; Pixel; Pattern recognition (psychology); Similarity (geometry); Noise reduction; Image (mathematics); Wavelet; Computer science; Image denoising; Computer vision; Noise (video); Feature (linguistics); Mathematics; Function (biology)","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":[0.0003702593,0.0002475546,0.00030127,0.0002046974,0.0004511391,0.00019028,0.0009035227,0.00008167508,0.00001988034],"category_scores_gemma":[0.00004027434,0.000221013,0.0001214072,0.0006978036,0.0001474516,0.0008352259,0.000402749,0.0002013244,0.0001279668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001408133,"about_ca_system_score_gemma":0.0001847198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002625311,"about_ca_topic_score_gemma":0.00000394036,"domain_scores_codex":[0.9978416,0.0001989473,0.0002906928,0.0006641386,0.0004619329,0.0005427178],"domain_scores_gemma":[0.9988446,0.00007585702,0.00005348037,0.0005999103,0.0001884878,0.0002377083],"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":[0.0002509009,0.001252029,0.0003214856,0.00007549483,0.0002051325,0.001491406,0.07492536,0.02953718,0.6903275,0.04390296,0.007647414,0.1500632],"study_design_scores_gemma":[0.000909646,0.0003047138,0.002132808,0.00003335913,0.00001988851,0.0008389045,0.0008550775,0.9742501,0.0187127,0.0009073372,0.0003410727,0.0006944464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0318573,0.00002678233,0.9405354,0.00009180007,0.0001447811,0.0002134412,7.008408e-7,0.0001859919,0.02694384],"genre_scores_gemma":[0.3237218,0.000002047505,0.6747999,0.000766822,0.00007992521,0.000004170824,4.867811e-7,0.0000163711,0.0006084904],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9447129,"threshold_uncertainty_score":0.9012649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05435581043843497,"score_gpt":0.2817364096959204,"score_spread":0.2273805992574854,"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."}}