{"id":"W2014131152","doi":"10.1117/12.387647","title":"&lt;title&gt;Blind deconvolution of human brain SPECT images using a distribution mixture estimation&lt;/title&gt;","year":2000,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Institut national de recherche en informatique et en automatique (INRIA)","keywords":"Deconvolution; Artificial intelligence; Overfitting; Computer science; Context (archaeology); Image resolution; Blind deconvolution; Pattern recognition (psychology); Noise (video); Computer vision; Algorithm; Image (mathematics); Artificial neural network","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":[0.0007795421,0.0005278471,0.0004671207,0.000888107,0.0002193004,0.0005398822,0.0005200925,0.0006077074,0.01909765],"category_scores_gemma":[0.001224625,0.0001626788,0.0003629036,0.0005960733,0.000544632,0.0007494622,0.0003337993,0.0003505505,0.00983596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003690935,"about_ca_system_score_gemma":0.0004300249,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001876669,"about_ca_topic_score_gemma":0.002530963,"domain_scores_codex":[0.9998084,0.00004325226,0.0000133284,0.00004090027,0.00008283769,0.00001130668],"domain_scores_gemma":[0.999545,0.0001293034,0.00004309209,0.0000950964,0.0001623864,0.00002501079],"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":[0.001422442,0.0001368934,0.0008478179,0.0005051678,0.00009059363,0.000655888,0.0000830886,0.05487498,0.252411,0.01874688,0.05084512,0.6193802],"study_design_scores_gemma":[0.00008238481,0.000289292,0.002199062,0.00004641939,0.00003961315,0.0008847159,0.00002409761,0.6445451,0.2842212,0.005709378,0.06185548,0.0001031441],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01709519,0.001098627,0.960269,0.0007104428,0.0005800232,0.000147876,0.0004847024,0.00593555,0.01367864],"genre_scores_gemma":[0.1667652,0.001903847,0.7400382,0.0002609239,0.0003927764,0.000118642,0.001538003,0.001120509,0.08786192],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01909765,"threshold_uncertainty_score":0.06388801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430537126419712,"score_gpt":0.2638189796717374,"score_spread":0.2495136084075403,"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."}}