{"id":"W2159026610","doi":"10.1109/igarss.2008.4778835","title":"SAR Image Filtering Via Learned Dictionaries and Sparse Representations","year":2008,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"Computer Research Institute of Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Deutsches Zentrum für Luft- und Raumfahrt; Ministère du Développement Économique, de l’Innovation et de l’Exportation","keywords":"Sparse approximation; K-SVD; Curvelet; Artificial intelligence; Computer science; Singular value decomposition; Pattern recognition (psychology); Matching pursuit; Noise (video); Transformation (genetics); Noise reduction; Image (mathematics); Computer vision; Algorithm; Compressed sensing; Wavelet transform; Wavelet","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005884573,0.0004006521,0.0007083241,0.0005785231,0.0001657784,0.0005665116,0.0004265606,0.0007837897,0.0008661019],"category_scores_gemma":[0.002240201,0.0003793727,0.0005096428,0.0005944712,0.0006150792,0.00103778,0.0006100813,0.0007287619,0.000395879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002671426,"about_ca_system_score_gemma":0.0003289758,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009294518,"about_ca_topic_score_gemma":0.001141043,"domain_scores_codex":[0.9996815,0.00008200099,0.00001892652,0.00006609507,0.0001298482,0.00002159693],"domain_scores_gemma":[0.9992194,0.000417099,0.0000988705,0.0001017725,0.000144386,0.00001846689],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001418563,0.00008036076,0.000872785,0.0001922841,0.0001256631,0.000131112,0.0001314549,0.6102982,0.03523606,0.07168961,0.002221712,0.278879],"study_design_scores_gemma":[0.000008829465,0.00002595849,0.0002092158,0.000007194285,0.000006586528,0.00003896968,0.000007938076,0.9864632,0.002786822,0.009463022,0.0009759207,0.000006304918],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007106891,0.0001398409,0.9917996,0.0001161259,0.00001378288,0.000009062395,0.00001613151,0.00009642405,0.0007022144],"genre_scores_gemma":[0.2905312,0.001238463,0.7019698,0.0002267166,0.0001695698,0.0001125439,0.0002978277,0.0000759454,0.005377981],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009294518,"threshold_uncertainty_score":0.003112137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05227211126827985,"score_gpt":0.304741003983016,"score_spread":0.2524688927147362,"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."}}