{"id":"W2947378481","doi":"10.29252/jgit.6.4.149","title":"Speckle Reduction in Synthetic Aperture Radar Images in Wavelet Domain Using Laplace Distribution","year":2019,"lang":"en","type":"article","venue":"Journal of Geospatial Information Technology","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Speckle noise; Speckle pattern; Synthetic aperture radar; Artificial intelligence; Wavelet; Computer science; Computer vision; Wavelet transform; Pattern recognition (psychology); Noise reduction; Noise (video); Maximum a posteriori estimation; Algorithm; Mathematics; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0008377954,0.0001068037,0.0002555284,0.0008203606,0.00003970044,0.00008264928,0.0004200122,0.0001958798,0.00001386972],"category_scores_gemma":[0.000237271,0.00009623299,0.00005147122,0.0008925708,0.00004266251,0.002383197,0.00009254753,0.0004639441,0.00002379074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002605251,"about_ca_system_score_gemma":0.0001119821,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004468602,"about_ca_topic_score_gemma":0.000004609818,"domain_scores_codex":[0.9986473,0.0001068333,0.00070015,0.00009326149,0.000241977,0.0002105207],"domain_scores_gemma":[0.9989976,0.00006024641,0.0005263453,0.0002177622,0.0001709265,0.00002717808],"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":[0.0007095163,0.0003906441,0.004297104,0.0002246638,0.00005488845,0.0001924106,0.005056092,0.009634465,0.1937552,0.04803447,0.001399925,0.7362506],"study_design_scores_gemma":[0.02315014,0.00354251,0.02701064,0.002244227,0.00005967889,0.01343772,0.006140423,0.3394906,0.3598247,0.1402459,0.08242003,0.002433394],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3397538,0.00006466713,0.657777,0.001786186,0.0003915688,0.0001156452,0.000002730475,0.00002439614,0.00008402079],"genre_scores_gemma":[0.8309171,0.00004085163,0.1688768,0.00009275262,0.00004209358,0.000001464651,0.00000648491,0.000004333007,0.00001812139],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7338172,"threshold_uncertainty_score":0.3924267,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005862485972804399,"score_gpt":0.2383403608871698,"score_spread":0.2324778749143654,"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."}}