{"id":"W2298382914","doi":"10.15353/vsnl.v1i1.59","title":"A Bayesian Joint Decorrelation and Despeckling approach for speckle reduction of SAR Images","year":2015,"lang":"en","type":"article","venue":"Vision Letters","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for the Central Universities; University of Science and Technology Beijing; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Decorrelation; Synthetic aperture radar; Speckle pattern; Speckle noise; Computer science; Artificial intelligence; Algorithm; Joint (building); Bayesian probability; Mathematics; Pattern recognition (psychology); Computer vision","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0008771587,0.00008388147,0.0001355962,0.0001467287,0.00006766559,0.0000935778,0.0001386047,0.00003628103,9.216153e-7],"category_scores_gemma":[0.00009235123,0.00007566081,0.00004620887,0.0001730121,0.00004402634,0.0004836032,0.00006079253,0.00006337461,0.000001763899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002954005,"about_ca_system_score_gemma":0.00001949362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001521777,"about_ca_topic_score_gemma":5.789274e-8,"domain_scores_codex":[0.9991322,0.00009580078,0.0002046825,0.0002597378,0.0001750103,0.0001326105],"domain_scores_gemma":[0.9994847,0.00005009311,0.000106762,0.0002137669,0.00007807748,0.00006657522],"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":[0.00009923487,0.00009358715,0.0002067728,0.00008393255,0.00002079652,0.000005465864,0.002565009,0.005865138,0.7895675,0.0020178,0.01799598,0.1814788],"study_design_scores_gemma":[0.002202718,0.00038434,0.001673333,0.00008421302,0.00002938816,0.0001090608,0.0002129601,0.8070219,0.1808645,0.005431726,0.001612716,0.0003730812],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03178457,0.0000924167,0.9662484,0.001195098,0.0002435847,0.0001675238,8.735136e-7,0.00004124761,0.0002263073],"genre_scores_gemma":[0.2865881,0.000002698959,0.7131057,0.0001666156,0.00008154652,0.000002671416,0.000003096734,0.000006435508,0.00004316369],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8011568,"threshold_uncertainty_score":0.3085358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03640427656365015,"score_gpt":0.2903807550312811,"score_spread":0.2539764784676309,"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."}}