{"id":"W2103882108","doi":"10.1186/1687-6180-2012-16","title":"SSIM-inspired image restoration using sparse representation","year":2012,"lang":"en","type":"article","venue":"EURASIP Journal on Advances in Signal Processing","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":74,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sparse approximation; Metric (unit); Image quality; Mean squared error; Computer science; Norm (philosophy); Gradient descent; Representation (politics); Artificial intelligence; Pattern recognition (psychology); Image restoration; Algorithm; Peak signal-to-noise ratio; Image (mathematics); Mathematics; Image processing; Statistics; Artificial neural network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005949019,0.0005511188,0.0006697579,0.0006786544,0.0001781738,0.0005556592,0.0006739345,0.0007272928,0.001227195],"category_scores_gemma":[0.001463364,0.0002329046,0.0006147645,0.0007483634,0.0005574174,0.0009381893,0.0008042786,0.000714026,0.0005150231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003321293,"about_ca_system_score_gemma":0.0003993465,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004882591,"about_ca_topic_score_gemma":0.0007159605,"domain_scores_codex":[0.9997306,0.00006231276,0.00001414845,0.00003070737,0.0001460277,0.00001615229],"domain_scores_gemma":[0.9995871,0.0001379743,0.00006139681,0.00006279365,0.0001309969,0.00001985442],"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":[0.0002065296,0.0001122019,0.0005850488,0.0004078553,0.0001034241,0.0002038389,0.0002207126,0.3760531,0.1420215,0.05917655,0.004154597,0.4167546],"study_design_scores_gemma":[0.000007895169,0.00004593287,0.0001202305,0.000009343112,0.000008931148,0.0001359344,0.00001146038,0.9760384,0.01551194,0.005958837,0.002140829,0.00001014762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006085368,0.0001569816,0.9924088,0.00008420229,0.00002390099,0.00001613013,0.00001444692,0.0001758629,0.001034311],"genre_scores_gemma":[0.169436,0.0005463863,0.8263031,0.0001494884,0.0000634689,0.00008168835,0.0001546528,0.0001107241,0.003154478],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001227195,"threshold_uncertainty_score":0.004105389,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04963724024173807,"score_gpt":0.3697160182222656,"score_spread":0.3200787779805275,"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."}}