{"id":"W2045147903","doi":"10.1137/130915406","title":"Total Variation Structured Total Least Squares Method for Image Restoration","year":2013,"lang":"en","type":"article","venue":"SIAM Journal on Scientific Computing","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":78,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mathematics; Total variation denoising; Image restoration; Minification; Regularization (linguistics); Algorithm; Function (biology); Image (mathematics); Mathematical optimization; Variation (astronomy); Least-squares function approximation; Applied mathematics; Point spread function; Image processing; Statistics; Computer science; Artificial intelligence","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":[0.00090486,0.0007308481,0.0007425072,0.0006184095,0.0002728631,0.0004826634,0.0009860331,0.001112688,0.001466458],"category_scores_gemma":[0.001884036,0.0003205977,0.0008837474,0.0007192152,0.000812119,0.0008028093,0.0008055158,0.001210035,0.0007427658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003666885,"about_ca_system_score_gemma":0.0006973951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001393994,"about_ca_topic_score_gemma":0.001342292,"domain_scores_codex":[0.9993749,0.0002192411,0.00001774405,0.0000794428,0.0002885517,0.00002017727],"domain_scores_gemma":[0.9994802,0.0002611232,0.00005518416,0.00005794153,0.0001285755,0.0000171047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008348468,0.00006153133,0.0004902912,0.0005960191,0.000155519,0.0001430346,0.0001741597,0.6175097,0.03004299,0.09507508,0.006233983,0.2494342],"study_design_scores_gemma":[0.000005290771,0.00002718117,0.00008929947,0.00001273123,0.00001013163,0.000070619,0.000008896697,0.9815949,0.002213912,0.01188343,0.004070214,0.00001347218],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007451958,0.0003481732,0.9983103,0.00004999151,0.00002152324,0.000007689293,0.000007170435,0.00006479033,0.0004451038],"genre_scores_gemma":[0.09566824,0.001807418,0.8956853,0.0001723608,0.0001234169,0.0001401827,0.0001402849,0.0002192447,0.006043532],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001466458,"threshold_uncertainty_score":0.00490576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03897622812694122,"score_gpt":0.3536789086436316,"score_spread":0.3147026805166904,"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."}}