{"id":"W2163801905","doi":"10.1109/crv.2009.14","title":"JEDI: Adaptive Stochastic Estimation for Joint Enhancement and Despeckling of Images for SAR","year":2009,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Synthetic aperture radar; Speckle pattern; Speckle noise; Computer vision; Image quality; Monte Carlo method; Pattern recognition (psychology); Mathematics; Image (mathematics); Statistics","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":[0.001029369,0.0007393397,0.0006305081,0.0006283706,0.0002435886,0.0004234427,0.00087827,0.0005218635,0.0006258067],"category_scores_gemma":[0.001995277,0.0004421494,0.0005766134,0.0004620368,0.0004751064,0.0006644144,0.0008997362,0.001045171,0.0003221038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004120103,"about_ca_system_score_gemma":0.0007146682,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001266707,"about_ca_topic_score_gemma":0.002576414,"domain_scores_codex":[0.9994227,0.0001052884,0.00002970016,0.00007344779,0.0003332047,0.0000357492],"domain_scores_gemma":[0.9994086,0.0002278267,0.0001065976,0.00007617955,0.0001439403,0.00003686217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003291062,0.0001948171,0.002284735,0.0003269199,0.0001612455,0.0001914321,0.0001891981,0.2766502,0.1701694,0.01670443,0.002611443,0.530187],"study_design_scores_gemma":[0.0000158442,0.00009165254,0.0006859364,0.000007945422,0.00001707773,0.0001687213,0.00001049691,0.9674311,0.02768774,0.001655027,0.002200305,0.00002809685],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003748014,0.00009412527,0.9956755,0.00002648573,0.00001343996,0.00001815471,0.00001296397,0.0002078753,0.0002035554],"genre_scores_gemma":[0.080778,0.0002534549,0.9173648,0.00004577985,0.00003679517,0.0000672403,0.0001612297,0.0000694267,0.001223383],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001266707,"threshold_uncertainty_score":0.005443931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04330686655999693,"score_gpt":0.3134616618385584,"score_spread":0.2701547952785615,"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."}}