{"id":"W2361393149","doi":"","title":"A Speckle Reduction Algorithm by Edge-Preserving for SAR Images","year":2000,"lang":"en","type":"article","venue":"","topic":"Optical Systems and Laser Technology","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"CAE (Canada)","funders":"","keywords":"Speckle pattern; Speckle noise; Artificial intelligence; Computer vision; Reduction (mathematics); Enhanced Data Rates for GSM Evolution; Synthetic aperture radar; Computer science; Filter (signal processing); Edge detection; Algorithm; Image (mathematics); Mathematics; Image processing","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00004567595,0.00007727127,0.0001024473,0.00002478947,0.00003411492,0.00002185852,0.00009286292,0.0000848824,0.001262336],"category_scores_gemma":[0.00000502437,0.00007023529,0.00003470372,0.00007442749,0.00001628014,0.00009051689,0.000008902659,0.00006348016,0.0001086484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002462271,"about_ca_system_score_gemma":0.00000174545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005953562,"about_ca_topic_score_gemma":0.000002037834,"domain_scores_codex":[0.9995267,0.000003400482,0.000119751,0.0001148385,0.00004354677,0.0001917208],"domain_scores_gemma":[0.9997861,0.00001183034,0.000005264659,0.0001476855,0.00001249853,0.00003657803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003902775,0.00003101607,0.00000839009,0.00006580762,0.0000361852,0.000001361747,0.0000302304,0.0006094331,0.02931463,0.000683889,0.4597479,0.5094673],"study_design_scores_gemma":[0.0003476757,0.00006246744,0.00002759159,0.00001582161,0.00001069301,0.00001944602,0.00009705207,0.1272281,0.09150858,0.001107405,0.7793501,0.0002250275],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.271511,0.003327751,0.3847513,0.002621809,0.001703279,0.001657379,0.0001835927,0.006369343,0.3278746],"genre_scores_gemma":[0.7658052,0.0002941496,0.1472419,0.00006875136,0.001038604,0.0001410325,0.00005894114,0.0001332104,0.08521826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5092423,"threshold_uncertainty_score":0.9996507,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004921608440002852,"score_gpt":0.2020492218901274,"score_spread":0.1971276134501245,"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."}}