{"id":"W2135591994","doi":"10.1109/newcas.2005.1496736","title":"A New Wavelet-based Method-for Despeckling SAR Images","year":2005,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Synthetic aperture radar; Speckle pattern; Speckle noise; Wavelet; Artificial intelligence; Noise (video); Pattern recognition (psychology); Computer science; Wavelet transform; Computer vision; Signal-to-noise ratio (imaging); Radar imaging; Enhanced Data Rates for GSM Evolution; Mathematics; Image (mathematics); Radar; Telecommunications","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":[0.001134532,0.0001418215,0.0001813299,0.0001178344,0.0001278235,0.0002720178,0.0006490438,0.00005252678,0.00009540996],"category_scores_gemma":[0.0001458071,0.0001191837,0.0001288271,0.0002648572,0.000012863,0.0004163288,0.00007530313,0.00009184732,0.00007370745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003945511,"about_ca_system_score_gemma":0.0001691752,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006111405,"about_ca_topic_score_gemma":0.000004309484,"domain_scores_codex":[0.9987499,0.0001285987,0.0002159033,0.000376378,0.0001912737,0.0003379495],"domain_scores_gemma":[0.9987004,0.0005805174,0.00005238903,0.0004408379,0.00009173378,0.000134103],"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":[0.00001237442,0.00001754971,0.000002250054,0.000007050698,0.000007194771,0.000004744206,0.00009003479,0.0002942464,0.02247508,0.0123776,0.0141993,0.9505126],"study_design_scores_gemma":[0.0009778546,0.00006485675,0.00004829361,0.0000124801,0.000009247973,0.00001101011,0.000004851777,0.4650339,0.4433068,0.006236999,0.08406378,0.0002299372],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00004274077,0.0001461042,0.9890336,0.004555577,0.0001575901,0.0001478415,7.597326e-7,0.0002575025,0.005658325],"genre_scores_gemma":[0.00122217,0.00000167462,0.9858298,0.003682013,0.0003276553,0.00000390989,9.345313e-7,0.0000129986,0.008918811],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9502826,"threshold_uncertainty_score":0.486017,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.030080102944501,"score_gpt":0.3318320299502069,"score_spread":0.3017519270057059,"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."}}