{"id":"W4250113389","doi":"10.32920/ryerson.14649948.v1","title":"Dictionaries and algorithms for sparsity constrained image reconstruction","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Algorithm; Signal reconstruction; SIGNAL (programming language); Iterative reconstruction; Coding (social sciences); Nonlinear system; Signal processing; Artificial intelligence; Peak signal-to-noise ratio; Polynomial; Exponential function; Image (mathematics); Pattern recognition (psychology); Mathematics; Digital signal processing; Statistics","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":[0.001458871,0.0007333359,0.0007897983,0.001088829,0.0003055472,0.00113257,0.0009604092,0.001264036,0.002898795],"category_scores_gemma":[0.005076765,0.0006084835,0.0008037029,0.001410554,0.001040895,0.001398471,0.001750751,0.002353777,0.00152392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005393709,"about_ca_system_score_gemma":0.0005635539,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001185046,"about_ca_topic_score_gemma":0.001349694,"domain_scores_codex":[0.9992597,0.0002203262,0.00006233913,0.0001299775,0.0002929853,0.00003468954],"domain_scores_gemma":[0.9981005,0.0009829256,0.0001696413,0.0003515061,0.0003469889,0.00004854805],"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":[0.0002191299,0.00007155539,0.0007799512,0.0006621649,0.000157706,0.0001127372,0.0002013242,0.3472371,0.02433032,0.2183671,0.007029275,0.4008317],"study_design_scores_gemma":[0.00002378739,0.00005312397,0.0002759956,0.00006284524,0.00001692218,0.000152379,0.00002849689,0.9391729,0.007935895,0.04222718,0.01002233,0.00002811315],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00144569,0.0003876018,0.996997,0.0001221368,0.00003314803,0.00002224501,0.00003959294,0.00008005909,0.0008725314],"genre_scores_gemma":[0.04899985,0.001862802,0.9437581,0.0001634931,0.0001474197,0.0001624295,0.0003661525,0.0001841993,0.004355437],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002898795,"threshold_uncertainty_score":0.009697378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04015180165083621,"score_gpt":0.2952026269206449,"score_spread":0.2550508252698087,"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."}}