{"id":"W1838275922","doi":"10.1109/iscas.2006.1692936","title":"Wavelet-based spatially adaptive method for despeckling SAR images","year":2006,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Speckle noise; Wavelet; Speckle pattern; Maximum a posteriori estimation; Artificial intelligence; Synthetic aperture radar; Computer science; Estimator; Gaussian noise; Additive white Gaussian noise; Pattern recognition (psychology); Noise (video); Gaussian; Mathematics; Minimum mean square error; White noise; Mean squared error; Wavelet transform; Algorithm; Image (mathematics); Statistics; Physics; Maximum likelihood","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":[0.001174811,0.0001644679,0.0002116175,0.0001227805,0.0001735124,0.0002322911,0.000545372,0.00005989747,0.00002166219],"category_scores_gemma":[0.0000940001,0.0001402381,0.0001412842,0.000251085,0.00002962797,0.0003096884,0.00007327824,0.00008762695,0.0000189473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004061691,"about_ca_system_score_gemma":0.0001194811,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003713338,"about_ca_topic_score_gemma":0.00001878588,"domain_scores_codex":[0.9985389,0.0002069881,0.0002532321,0.0004283076,0.0002220354,0.0003505716],"domain_scores_gemma":[0.9984156,0.0008592595,0.00008296587,0.0003776522,0.0002106913,0.00005383176],"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":[0.0001132999,0.0001545236,0.00002185774,0.00003206766,0.00002799643,0.00005738981,0.0001097602,0.003598941,0.111251,0.1709109,0.008434032,0.7052883],"study_design_scores_gemma":[0.0008101096,0.0001356884,0.0001940044,0.000012129,0.0000106348,0.000005690054,0.000004753254,0.5739014,0.3904943,0.03096935,0.003236439,0.000225493],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00009200215,0.0000712663,0.9890734,0.0007371142,0.0001945048,0.0002353457,0.000003638083,0.0002246548,0.009368089],"genre_scores_gemma":[0.01601881,4.542066e-7,0.9808043,0.0009097013,0.0001852007,0.00001181353,0.000003397265,0.00001544371,0.002050891],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7050627,"threshold_uncertainty_score":0.5718744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02576386999444456,"score_gpt":0.30243673830455,"score_spread":0.2766728683101055,"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."}}