{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008060034,0.000799007,0.000764086,0.0009559919,0.0003706897,0.0009194047,0.0005576738,0.0008784897,0.001321061],"category_scores_gemma":[0.001895508,0.0004052208,0.0008932707,0.0008222461,0.0005505562,0.001398226,0.0008257283,0.001134212,0.0009741556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003400085,"about_ca_system_score_gemma":0.0004188995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006279465,"about_ca_topic_score_gemma":0.0009059583,"domain_scores_codex":[0.9996238,0.00006868412,0.00002621543,0.00006398408,0.000184462,0.00003280846],"domain_scores_gemma":[0.9993444,0.0002186262,0.0001008834,0.0001503333,0.0001557807,0.00002988638],"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":[0.0005010533,0.00007733719,0.0009591953,0.0004144053,0.0001249241,0.0002818482,0.0002290624,0.05259271,0.3851464,0.02129444,0.002041359,0.5363372],"study_design_scores_gemma":[0.00003460873,0.0002617183,0.00158898,0.00005320245,0.0001070586,0.0008873115,0.00007015627,0.6226455,0.3496872,0.009357459,0.01524289,0.00006399484],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01471683,0.0004997025,0.9835657,0.00009792845,0.00007190698,0.00002124585,0.00003079269,0.000298018,0.0006978257],"genre_scores_gemma":[0.1033306,0.001291975,0.8923662,0.0001026191,0.00009391768,0.00003164785,0.0001452189,0.0001105634,0.002527331],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001321061,"threshold_uncertainty_score":0.004419327,"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."}}