{"id":"W2105213642","doi":"10.1109/pacrim.1997.619960","title":"Denoising of low SNR signals using composite wavelet shrinkage","year":2002,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Thresholding; Clipping (morphology); Noise reduction; Shrinkage; White noise; Wavelet; Algorithm; Mathematics; Noise (video); Computer science; Threshold limit value; Pattern recognition (psychology); Signal-to-noise ratio (imaging); Artificial intelligence; 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":[0.0009700684,0.0005251847,0.0006953196,0.0008497848,0.0001932694,0.0006469169,0.0004804731,0.0005204756,0.0008531579],"category_scores_gemma":[0.002354715,0.0002286278,0.0005480844,0.0005258532,0.0005525013,0.0008184255,0.0007690972,0.0008871209,0.0003527566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002021104,"about_ca_system_score_gemma":0.0003309567,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002307759,"about_ca_topic_score_gemma":0.0005631192,"domain_scores_codex":[0.9995136,0.00007515783,0.00002605691,0.00008752365,0.0002751039,0.00002245368],"domain_scores_gemma":[0.9991763,0.0003295428,0.0001087821,0.0001188399,0.0002165482,0.00004997451],"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":[0.0004287587,0.00007946514,0.001478937,0.0003967803,0.00009859753,0.0002727058,0.0001857918,0.06768429,0.5145194,0.01955461,0.001448679,0.393852],"study_design_scores_gemma":[0.00003273344,0.0003262644,0.002815418,0.00003438952,0.00008360339,0.0006672475,0.00004201883,0.7428244,0.2360929,0.008506534,0.008513501,0.00006102164],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01999354,0.0002187495,0.9785259,0.00004159857,0.00004678864,0.00001713013,0.00001750649,0.0002449189,0.0008939508],"genre_scores_gemma":[0.2237985,0.0006424985,0.7731443,0.00006375377,0.00008763728,0.00003958434,0.0000961082,0.0001196017,0.002007917],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009700684,"threshold_uncertainty_score":0.005130231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04522831330821078,"score_gpt":0.2821489776184244,"score_spread":0.2369206643102136,"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."}}