{"id":"W2001574155","doi":"10.1109/iembs.2011.6090973","title":"Wavelet-based ultrasound image denoising: Performance analysis and comparison","year":2011,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Curvelet; Speckle noise; Contourlet; Artificial intelligence; Wavelet; Speckle pattern; Computer science; Noise reduction; Computer vision; Complex wavelet transform; Multiplicative noise; Noise (video); Pattern recognition (psychology); Wavelet transform; Image (mathematics); Discrete wavelet transform","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":[0.000599276,0.0001523614,0.0002707729,0.0002897893,0.0001768212,0.0002349776,0.0004986602,0.00005000141,0.0001100435],"category_scores_gemma":[0.00004411051,0.0001270614,0.00008908611,0.0009604463,0.00009576415,0.0005582436,0.00008614253,0.0001197626,0.00003886232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001748987,"about_ca_system_score_gemma":0.00003357247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000131734,"about_ca_topic_score_gemma":0.00001484012,"domain_scores_codex":[0.9987447,0.0001209114,0.0002474947,0.0003849587,0.0002193382,0.0002825714],"domain_scores_gemma":[0.9990127,0.0001789698,0.00007961975,0.000521162,0.00009437851,0.0001131645],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002199637,0.001106793,0.2799055,0.0001799227,0.001394109,0.0001675147,0.01039626,0.0001128455,0.1493967,0.01438673,0.002671802,0.5400618],"study_design_scores_gemma":[0.0008432253,0.0002115726,0.3034716,0.00001284637,0.0002844792,0.00002311139,0.00004949385,0.3145453,0.3789836,0.0007353537,0.0003263606,0.0005130139],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1907233,0.0000519049,0.7994004,0.00003838519,0.00005514384,0.00004826549,3.935069e-7,0.0001179171,0.009564268],"genre_scores_gemma":[0.5515237,0.000004147376,0.4479999,0.0002150672,0.000009799828,0.00000164346,0.000001130997,0.000004132363,0.0002404685],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5395488,"threshold_uncertainty_score":0.5181413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03657534818258711,"score_gpt":0.276175804872039,"score_spread":0.2396004566894519,"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."}}