{"id":"W2133347330","doi":"10.1109/crv.2012.30","title":"Image De-blurring Using Shearlets","year":2012,"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":"University of Alberta","funders":"","keywords":"Deblurring; Shearlet; Classification of discontinuities; Computer science; Artificial intelligence; Wavelet; Shear (geology); Wavelet transform; Computer vision; Image (mathematics); Algorithm; Pattern recognition (psychology); Image restoration; Image processing; Mathematics; Geology; 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":[0.0007925328,0.00006903308,0.00007571551,0.00005590717,0.00009603956,0.000155689,0.0003139772,0.00002870233,0.00005107926],"category_scores_gemma":[0.0000432276,0.00006037008,0.00003933502,0.0001689004,0.00001711995,0.001088444,0.0001604398,0.00007584397,0.00008717085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003671356,"about_ca_system_score_gemma":0.00002884604,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005333993,"about_ca_topic_score_gemma":2.059412e-7,"domain_scores_codex":[0.9991835,0.00009730775,0.00009316132,0.0001189418,0.0001225135,0.0003845149],"domain_scores_gemma":[0.999517,0.00005930616,0.00002084437,0.0002600182,0.00002858141,0.0001141979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005669088,0.000104529,0.004042294,0.00002389478,0.00001717174,0.00004776672,0.002451084,0.00004731313,0.8402531,0.03948706,0.001524653,0.1119955],"study_design_scores_gemma":[0.0009237366,0.00004916271,0.01364819,0.00005024493,0.00002177589,0.0006159741,0.00007576985,0.5611911,0.4059272,0.008138315,0.008573155,0.0007854013],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03671587,0.0001144986,0.942219,0.00009962625,0.0002634165,0.00002921822,5.974795e-8,0.0001206818,0.02043761],"genre_scores_gemma":[0.2852684,8.156071e-7,0.7139037,0.0004439219,0.0001186756,5.992079e-7,5.056214e-8,0.000004666279,0.0002591304],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5611438,"threshold_uncertainty_score":0.246182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04640817776891284,"score_gpt":0.3276937067911339,"score_spread":0.281285529022221,"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."}}