{"id":"W2116882018","doi":"10.1109/icip.2007.4379368","title":"Locally Adaptive Wavelet-Based Image Denoising using the Gram-Charlier Prior Function","year":2007,"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":"Wavelet; Non-local means; Mathematics; Probability density function; Pattern recognition (psychology); Maximum a posteriori estimation; Estimator; Artificial intelligence; Wavelet transform; Noise reduction; Gaussian; Additive white Gaussian noise; Gaussian noise; Computer science; Algorithm; Statistics; White noise; Image denoising; 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":[0.002444388,0.0001997394,0.0001674747,0.0001451383,0.0005007113,0.0004042309,0.0006138111,0.00008408057,0.00004002325],"category_scores_gemma":[0.0000849496,0.0001351985,0.000119607,0.0006008658,0.0001204546,0.0006466475,0.00014682,0.0002565116,0.00005312197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001077985,"about_ca_system_score_gemma":0.0001514398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001252387,"about_ca_topic_score_gemma":0.0000191653,"domain_scores_codex":[0.9980968,0.0002104087,0.0003133993,0.000423516,0.0004546586,0.0005011809],"domain_scores_gemma":[0.9985125,0.00041454,0.0001164783,0.0005919546,0.0002605122,0.000103993],"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":[0.0002603933,0.000115675,0.00003520679,0.00001503887,0.0000481416,0.0001785481,0.0006751167,0.0002867797,0.262956,0.02049502,0.0004340973,0.7145],"study_design_scores_gemma":[0.001825779,0.0004129065,0.00433585,0.0000867752,0.0000758398,0.0001121891,0.000325278,0.6447538,0.3363019,0.008314803,0.0026702,0.0007846797],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008061501,0.0000883535,0.9875442,0.0003238583,0.0004750383,0.0002092407,3.571644e-7,0.0001944066,0.003103092],"genre_scores_gemma":[0.2416005,7.171992e-7,0.7553554,0.00246795,0.0001573643,0.000001843186,6.566041e-7,0.00001718445,0.0003984543],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7137153,"threshold_uncertainty_score":0.5513234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03337149519175172,"score_gpt":0.288565551514411,"score_spread":0.2551940563226593,"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."}}