{"id":"W2548055594","doi":"10.1109/newcas.2016.7604754","title":"Hybrid Wiener and partial differential equations filter for biomedical image denoising","year":2016,"lang":"en","type":"article","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; École de Technologie Supérieure","funders":"","keywords":"Wiener filter; Filter (signal processing); Noise reduction; Non-local means; Gaussian noise; Wiener deconvolution; Mathematics; Noise (video); Computer science; Partial differential equation; Algorithm; Artificial intelligence; Computer vision; Image denoising; Image (mathematics); Pattern recognition (psychology); Mathematical analysis","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":[0.0002836979,0.0001036558,0.0001244626,0.00008114693,0.0001531098,0.000214195,0.0002718229,0.00003298006,0.00009538019],"category_scores_gemma":[0.0002173627,0.00006142871,0.00005783144,0.00006536875,0.0001174869,0.0005318263,0.000167935,0.00003584861,0.00002353486],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001348554,"about_ca_system_score_gemma":0.0000340843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005775657,"about_ca_topic_score_gemma":4.473784e-7,"domain_scores_codex":[0.9989597,0.00007882373,0.0001835715,0.0003203802,0.0001797881,0.0002777822],"domain_scores_gemma":[0.9990021,0.0005269272,0.00003661249,0.0002515365,0.00006371243,0.0001191058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002995832,0.00007938249,0.00001401501,0.00001189758,0.00002361201,0.00002162742,0.0001408451,2.745672e-7,0.575651,0.06389404,0.005657387,0.3544759],"study_design_scores_gemma":[0.006283385,0.0004974586,0.0006989923,0.00009597012,0.00006660652,0.0001389137,0.000009104631,0.2152079,0.7032043,0.05301902,0.01990702,0.000871296],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0109497,0.00001753208,0.9864026,0.00184855,0.000362655,0.0001112605,0.00000701816,0.00009141528,0.0002092097],"genre_scores_gemma":[0.7103052,0.000003064708,0.2873185,0.0003391225,0.0002977004,0.0000209773,0.000002902625,0.00001005874,0.001702467],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6993555,"threshold_uncertainty_score":0.250499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02965731938670497,"score_gpt":0.294712304559728,"score_spread":0.265054985173023,"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."}}