{"id":"W2094447054","doi":"10.1016/j.acha.2015.04.001","title":"Adaptive frame-based color image denoising","year":2015,"lang":"en","type":"article","venue":"Applied and Computational Harmonic Analysis","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Alberta","funders":"Natural Science Foundation of Zhejiang Province; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Pacific Institute for the Mathematical Sciences","keywords":"Mathematics; Image denoising; Noise reduction; Frame (networking); Artificial intelligence; Computer vision; Image (mathematics); Pattern recognition (psychology); Computer science","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":[0.0005763117,0.0001757822,0.0003160053,0.0003290086,0.000191941,0.0002968371,0.0003631971,0.00006102382,0.00001192363],"category_scores_gemma":[0.00002506957,0.0001660917,0.0001306655,0.001376598,0.0001056455,0.0002308153,0.000129248,0.0001408963,0.00004848974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006314304,"about_ca_system_score_gemma":0.0002111211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003753701,"about_ca_topic_score_gemma":0.000002021339,"domain_scores_codex":[0.9984449,0.0001083536,0.0002617541,0.0004771354,0.0004631729,0.0002447465],"domain_scores_gemma":[0.998858,0.0003386466,0.0001239035,0.0002388763,0.0002401556,0.0002003758],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003476455,0.0003571938,0.000481813,0.00002325409,0.002055305,0.0001199623,0.002202265,0.5375274,0.002930188,0.3609728,0.001738284,0.09124388],"study_design_scores_gemma":[0.001023527,0.000054585,0.001694717,0.000003786022,0.0002742366,0.000004468429,0.00009736594,0.9319028,0.0007698742,0.06377523,0.0001633927,0.000235996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.017402,0.0002080635,0.9796917,0.000515601,0.00003830122,0.00009428984,0.000003473505,0.0001098989,0.001936628],"genre_scores_gemma":[0.6132705,0.000001113553,0.3858536,0.0007817487,0.00002477088,0.000009946481,0.00001479598,0.000005697261,0.000037768],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5958685,"threshold_uncertainty_score":0.6773024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.031478241349685,"score_gpt":0.2770288549628068,"score_spread":0.2455506136131218,"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."}}