{"id":"W2169183970","doi":"10.1109/mmsp.2006.285344","title":"Image Denoising Based on A Mixture of Bivariate Laplacian Models in Complex Wavelet Domain","year":2006,"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":"Queen's University","funders":"","keywords":"Wavelet; Bivariate analysis; Estimator; Pattern recognition (psychology); Maximum a posteriori estimation; Context (archaeology); Noise reduction; Wavelet transform; Independence (probability theory); Artificial intelligence; Probability density function; Mathematics; Computer science; Image (mathematics); Noise (video); Laplace operator; Algorithm; Statistics; Maximum likelihood","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":[0.0008502019,0.0001720412,0.0002791555,0.0003182904,0.00006526947,0.0001291402,0.0005681362,0.00007745074,0.00003018798],"category_scores_gemma":[0.00002105978,0.0001483249,0.00008531399,0.0006337906,0.00005089104,0.0003780387,0.00008811158,0.0001540071,0.00001125185],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004731628,"about_ca_system_score_gemma":0.00006348221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000552542,"about_ca_topic_score_gemma":0.00006362082,"domain_scores_codex":[0.9982921,0.000314329,0.000361065,0.0003652291,0.0003357709,0.0003314513],"domain_scores_gemma":[0.9990705,0.0002017199,0.00009646271,0.0005101885,0.00007409229,0.00004706235],"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":[0.000235632,0.0008767914,0.0002281899,0.0001208274,0.00001741055,0.0005572474,0.000980214,0.03680332,0.5763254,0.3566671,0.005650203,0.0215377],"study_design_scores_gemma":[0.001280383,0.00007761503,0.002261044,0.00004789711,0.000002894462,0.000006620683,0.000009025716,0.8759042,0.01891832,0.1010631,0.0002135316,0.0002153531],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0121265,0.00001464639,0.9513922,0.0006993808,0.00005366566,0.0001288121,0.000003713761,0.00007649895,0.0355046],"genre_scores_gemma":[0.4323069,2.771172e-7,0.5669292,0.0005939708,0.0000231407,0.000002086954,0.000004402452,0.00000818174,0.000131918],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8391009,"threshold_uncertainty_score":0.6048512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02186876319432177,"score_gpt":0.2640896770343775,"score_spread":0.2422209138400558,"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."}}