{"id":"W2119403778","doi":"10.4208/cicp.310811.090312a","title":"The Convex Relaxation Method on Deconvolution Model with Multiplicative Noise","year":2012,"lang":"en","type":"article","venue":"Communications in Computational Physics","topic":"Sparse and Compressive Sensing Techniques","field":"Engineering","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Deblurring; Deconvolution; Relaxation (psychology); Regular polygon; Multiplicative noise; Convex optimization; Mathematical optimization; Convex analysis; Noise (video); Multiplicative function; Computer science; Applied mathematics; Mathematics; Algorithm; Mathematical analysis; Image restoration; Artificial intelligence; Image processing; Image (mathematics); Geometry","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":[0.0001855934,0.000107555,0.00009190399,0.00003995807,0.0002338003,0.00002543428,0.0003326748,0.00003781354,4.500876e-7],"category_scores_gemma":[0.00002248117,0.00008811153,0.0000262563,0.0002194225,0.0001012287,0.0001787124,0.00006481246,0.0002392945,0.00001601902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001219447,"about_ca_system_score_gemma":0.00002298905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005930827,"about_ca_topic_score_gemma":0.000007020762,"domain_scores_codex":[0.9993757,0.00009233102,0.0001683209,0.00008619734,0.0001300782,0.0001473603],"domain_scores_gemma":[0.9983131,0.0008148218,0.00006659593,0.0006530912,0.00012172,0.00003064808],"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":[0.00000690938,0.00006017132,0.0002565884,0.000001540954,0.00001593289,3.106651e-8,0.0003470166,0.856724,0.0001104114,0.1296825,0.000310412,0.01248449],"study_design_scores_gemma":[0.0001438561,0.00001052102,0.003724298,0.00002936823,0.000007183168,0.000001507623,0.00004588241,0.9416613,0.0006803147,0.0530646,0.0005250549,0.000106092],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009145178,0.0003048246,0.9855455,0.0003957062,0.00003794599,0.000249128,0.000006084609,0.0002307172,0.00408492],"genre_scores_gemma":[0.8031166,0.0000724659,0.1965498,0.00007032518,0.00002780143,0.0000868855,0.00004785412,0.00001787509,0.00001036647],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7939715,"threshold_uncertainty_score":0.3593084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05531686571193842,"score_gpt":0.3290506210491289,"score_spread":0.2737337553371905,"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."}}