{"id":"W4285070320","doi":"10.1109/lgrs.2022.3185557","title":"SAR Despeckling Based on CNN and Bayesian Estimator in Complex Wavelet Domain","year":2022,"lang":"en","type":"article","venue":"IEEE Geoscience and Remote Sensing Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; Centre For Cold Ocean Resources Engineering; Institut National de la Recherche Scientifique","funders":"","keywords":"Wavelet; Artificial intelligence; Computer science; Convolutional neural network; Pattern recognition (psychology); Speckle noise; Speckle pattern; Synthetic aperture radar; Estimator; Noise reduction; Shrinkage estimator; Wavelet transform; Maximum a posteriori estimation; Mathematics; Bias of an estimator; Statistics; Minimum-variance unbiased estimator; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005007918,0.0005743199,0.000559664,0.0004974543,0.000186202,0.0003995612,0.0006355809,0.0005629025,0.0008519442],"category_scores_gemma":[0.0008136278,0.00032939,0.0005362473,0.000380511,0.0003217802,0.000799938,0.0005615437,0.0006088457,0.0003435624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004047648,"about_ca_system_score_gemma":0.0005927475,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003493895,"about_ca_topic_score_gemma":0.006034111,"domain_scores_codex":[0.9997247,0.00002605759,0.00001634814,0.00006423947,0.0001345006,0.00003407534],"domain_scores_gemma":[0.9997405,0.00006086837,0.00004072562,0.00003974243,0.000104012,0.00001404143],"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":[0.0002461742,0.00009476228,0.00212086,0.000146285,0.0001255988,0.0001910947,0.00009571304,0.3173887,0.1350979,0.01072693,0.001792826,0.5319731],"study_design_scores_gemma":[0.000007052856,0.00003515837,0.0005768403,0.000005997338,0.00001701939,0.0001110044,0.000008083224,0.9744418,0.02231869,0.001245485,0.001220654,0.0000122814],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01388923,0.0001647097,0.9847263,0.00007263886,0.00002446772,0.00001461848,0.00002878678,0.0002162208,0.0008632098],"genre_scores_gemma":[0.3032399,0.0005363547,0.6900438,0.0002001409,0.00007102478,0.0000705815,0.0002648085,0.00009816406,0.005475176],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003493895,"threshold_uncertainty_score":0.00694716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02005916016192574,"score_gpt":0.2570432534528794,"score_spread":0.2369840932909537,"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."}}