{"id":"W1925843279","doi":"10.1109/icip.2001.958076","title":"Wavelet shrinkage with correlated wavelet coefficients","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Wavelet; Shrinkage; Shrinkage estimator; Wavelet transform; Second-generation wavelet transform; Artificial intelligence; Wavelet packet decomposition; Cascade algorithm; Pattern recognition (psychology); Computer science; Stationary wavelet transform; Discrete wavelet transform; Context (archaeology); Mathematics; Algorithm; Statistics; Mean squared error; Geology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002805746,0.0001547815,0.0001702974,0.0001028655,0.0001463783,0.0002088379,0.0006725668,0.00006336642,0.0003375812],"category_scores_gemma":[0.0000321605,0.0001158919,0.00004399519,0.000622046,0.00005522936,0.0003332988,0.0001387309,0.0001779278,0.0005751628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002448093,"about_ca_system_score_gemma":0.00001439042,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001543772,"about_ca_topic_score_gemma":0.000001565064,"domain_scores_codex":[0.9985805,0.0001084971,0.0001830739,0.0003931586,0.0003585789,0.0003761766],"domain_scores_gemma":[0.9990635,0.00009923967,0.00005196257,0.0005702241,0.00009619467,0.0001189127],"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":[0.00004445082,0.0007342508,0.0003178287,0.00002981991,0.00009920895,0.001314042,0.002965224,0.0004359976,0.00616176,0.04641522,0.03988808,0.9015941],"study_design_scores_gemma":[0.002859852,0.0005045399,0.001657835,0.00005281103,0.00001802259,0.0003569218,0.00002514551,0.9452533,0.01841995,0.0005664627,0.029587,0.0006981585],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008158072,0.00005470567,0.9237095,0.0003955064,0.0002244424,0.00010798,5.337001e-7,0.0003062904,0.06704298],"genre_scores_gemma":[0.6026628,0.000009718492,0.3626156,0.001830681,0.00004253738,0.000005447089,0.00000214381,0.00001943946,0.03281164],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9448173,"threshold_uncertainty_score":0.7392746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01974985622765181,"score_gpt":0.2316239709897759,"score_spread":0.2118741147621241,"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."}}