{"id":"W2121673205","doi":"10.1109/icip.2002.1038155","title":"Fractal-wavelet image denoising","year":2003,"lang":"en","type":"article","venue":"Proceedings - International Conference on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wavelet; Fractal transform; Fractal compression; Artificial intelligence; Fractal; Wavelet transform; Mathematics; Smoothing; Image compression; Computer vision; Pattern recognition (psychology); Fractal analysis; Computer science; Image processing; Algorithm; Image (mathematics); Fractal dimension; Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005302421,0.000520761,0.0007966221,0.0006560867,0.0003055216,0.0005862545,0.0007392358,0.001173904,0.001838739],"category_scores_gemma":[0.001013706,0.0002622053,0.0008940076,0.0004911171,0.0006510697,0.0009362114,0.0008763135,0.0009533573,0.001299272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003958562,"about_ca_system_score_gemma":0.0002739454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003719396,"about_ca_topic_score_gemma":0.0004556895,"domain_scores_codex":[0.9995509,0.00005532882,0.00002263374,0.00006046616,0.0002779388,0.00003267873],"domain_scores_gemma":[0.9997274,0.00005200796,0.00002778449,0.00007208708,0.00009871796,0.00002205342],"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":[0.0002207954,0.0001253563,0.001191526,0.0005567608,0.0001645795,0.0006965254,0.0002238843,0.06321932,0.3060881,0.1944244,0.009962336,0.4231264],"study_design_scores_gemma":[0.000077011,0.0002949643,0.00151397,0.00007653312,0.00009789706,0.002877315,0.00003799838,0.6787758,0.1595126,0.05068805,0.1059547,0.00009315906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01016776,0.001591029,0.9819526,0.0002951806,0.0002957028,0.00006258991,0.00005900326,0.0005265442,0.005049618],"genre_scores_gemma":[0.2113763,0.002913216,0.7662892,0.0004809157,0.0003268711,0.0001278618,0.0003807514,0.0001971766,0.01790773],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001838739,"threshold_uncertainty_score":0.006151199,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04324623115660226,"score_gpt":0.3238802515222882,"score_spread":0.280634020365686,"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."}}