{"id":"W2257701136","doi":"10.15866/irecos.v8i10.3553","title":"Computed Tomography Images Restoration Using Anisotropic Diffusion Regularization","year":2013,"lang":"en","type":"article","venue":"International Review on Computers and Software (IRECOS)","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Deconvolution; Anisotropic diffusion; Computer science; Artificial intelligence; Computer vision; Image restoration; Blind deconvolution; Regularization (linguistics); Image quality; Image resolution; Inverse problem; Iterative reconstruction; Smoothness; Image (mathematics); Algorithm; Image processing; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006478149,0.000648882,0.0006892679,0.0009309785,0.0002922138,0.0006333251,0.0005268297,0.0009753695,0.000718076],"category_scores_gemma":[0.001845469,0.0003064084,0.001060532,0.0006753319,0.0005489272,0.0005401187,0.0007231054,0.0008980305,0.0003800303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003957745,"about_ca_system_score_gemma":0.0008256205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002525044,"about_ca_topic_score_gemma":0.001860032,"domain_scores_codex":[0.9995525,0.0001042509,0.00003100014,0.00008463261,0.0002030211,0.0000245425],"domain_scores_gemma":[0.9995602,0.0001365322,0.00006978653,0.00007470525,0.000138486,0.00002027505],"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":[0.0003507991,0.0001198069,0.0009779187,0.0005270778,0.0002313316,0.0004076665,0.0002801693,0.2314719,0.2645927,0.02103385,0.004514691,0.4754921],"study_design_scores_gemma":[0.0000270958,0.00006203623,0.0006854425,0.00002227153,0.00004012834,0.0004477594,0.00001817004,0.9500324,0.03781627,0.004825727,0.00598814,0.00003462283],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006746007,0.0004202866,0.9917503,0.0001296297,0.00003080654,0.00003001861,0.00002249981,0.0002662549,0.0006042925],"genre_scores_gemma":[0.1182486,0.001083156,0.8775752,0.00009699633,0.00006314411,0.00008404906,0.0001405944,0.000128582,0.002579744],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002525044,"threshold_uncertainty_score":0.005020678,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01929353837357904,"score_gpt":0.2786098436306268,"score_spread":0.2593163052570477,"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."}}