{"id":"W1981595564","doi":"10.1142/s0219691309002945","title":"IMAGE DENOISING BASED ON WAVELET SHRINKAGE USING NEIGHBOR AND LEVEL DEPENDENCY","year":2009,"lang":"en","type":"article","venue":"International Journal of Wavelets Multiresolution and Information Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Space Agency; Concordia University","funders":"","keywords":"Wavelet; Thresholding; Shrinkage; Noise reduction; Mathematics; Estimator; Pattern recognition (psychology); Dependency (UML); Algorithm; Artificial intelligence; Noise (video); Image (mathematics); Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009898645,0.0004098884,0.0008393164,0.0008383097,0.0002790156,0.0005127344,0.0005910185,0.0006718218,0.0007070323],"category_scores_gemma":[0.002118221,0.0004106289,0.001009141,0.0007246607,0.0005478614,0.001003005,0.0007974295,0.0006030411,0.0002556698],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003045509,"about_ca_system_score_gemma":0.0003308847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008372526,"about_ca_topic_score_gemma":0.001309245,"domain_scores_codex":[0.9995369,0.00007748285,0.00003429825,0.0001015209,0.000220176,0.00002963424],"domain_scores_gemma":[0.9993836,0.0002371059,0.00006741851,0.0001046278,0.0001804528,0.00002675161],"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":[0.0003937383,0.0001143566,0.002793813,0.0003542711,0.0001960214,0.0002396486,0.0003585423,0.1558827,0.2145935,0.03136894,0.001457186,0.5922472],"study_design_scores_gemma":[0.00001955099,0.0001046718,0.001534131,0.00001708292,0.00006953807,0.0002952247,0.00002796344,0.9435818,0.04511593,0.00662668,0.002569103,0.00003834518],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02619085,0.0002580127,0.9725137,0.00004709518,0.00002753718,0.00001964459,0.00001435899,0.0001564789,0.0007722552],"genre_scores_gemma":[0.2900656,0.0007141809,0.7067668,0.00006515151,0.00005417295,0.00006375412,0.00009266091,0.0001165366,0.002061066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009898645,"threshold_uncertainty_score":0.005235016,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02731479245890638,"score_gpt":0.3041720487839867,"score_spread":0.2768572563250803,"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."}}