{"id":"W2152521507","doi":"10.1109/nsip.2005.1502257","title":"Spatially adaptive multiscale thresholding for speckle and mixed noise removal","year":2005,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Speckle noise; Multiplicative noise; Wavelet transform; Wavelet; Noise (video); Artificial intelligence; Computer science; Thresholding; Discrete wavelet transform; Stationary wavelet transform; Second-generation wavelet transform; Pattern recognition (psychology); Speckle pattern; Noise reduction; Algorithm; Noise measurement; A priori and a posteriori; Computer vision; Image (mathematics)","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":[0.0002032936,0.0002008151,0.000295897,0.0002671446,0.0001182188,0.0002457804,0.0002736977,0.0002657966,0.006485],"category_scores_gemma":[0.0007673996,0.00008927414,0.000195231,0.0004855868,0.0002012517,0.000260735,0.0003140898,0.0002117575,0.001178367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000137874,"about_ca_system_score_gemma":0.0001244199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003425484,"about_ca_topic_score_gemma":0.000879543,"domain_scores_codex":[0.9998618,0.00001753065,0.000006089745,0.00002489672,0.00008216563,0.000007534547],"domain_scores_gemma":[0.9998118,0.00008024845,0.00001711197,0.00002797149,0.00005357575,0.000009130319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001143147,0.00003180198,0.0006407247,0.000392292,0.00004657838,0.000357012,0.0000951325,0.03921343,0.2859078,0.02279544,0.01029388,0.6401116],"study_design_scores_gemma":[0.00002323689,0.0002069322,0.002998376,0.00005540183,0.00009205516,0.0008775135,0.00004063595,0.8363825,0.09142201,0.01583746,0.05203173,0.00003218485],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04042839,0.00308473,0.9436234,0.0003447201,0.0003973833,0.00003478428,0.00007976131,0.0006460559,0.01136072],"genre_scores_gemma":[0.4599403,0.003137769,0.5076668,0.0002201396,0.0004100773,0.00008142604,0.0002009365,0.0002856204,0.0280569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006485,"threshold_uncertainty_score":0.02169442,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03462482632878464,"score_gpt":0.2857290371024265,"score_spread":0.2511042107736418,"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."}}