{"id":"W3083358456","doi":"10.1016/j.jvcir.2020.102895","title":"Gray-level image denoising with an improved weighted sparse coding","year":2020,"lang":"en","type":"article","venue":"Journal of Visual Communication and Image Representation","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Noise reduction; Neural coding; Pattern recognition (psychology); Artificial intelligence; Mathematics; Prior probability; Computer science; Coding (social sciences); Sparse approximation; Non-local means; Image (mathematics); Image denoising; Video denoising; Algorithm; 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":[0.0007813768,0.0006959866,0.0006930075,0.0007188938,0.0002382862,0.0005620779,0.0007293254,0.001028206,0.00144506],"category_scores_gemma":[0.002108965,0.0002986225,0.0008028215,0.0009615001,0.0005292277,0.001116365,0.001169199,0.001042838,0.0004617927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002561737,"about_ca_system_score_gemma":0.0006659454,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001661325,"about_ca_topic_score_gemma":0.002168957,"domain_scores_codex":[0.9995554,0.0001002348,0.00003241208,0.00007178684,0.000210548,0.00002959158],"domain_scores_gemma":[0.9993523,0.000179517,0.00004977667,0.0001328491,0.0002536044,0.00003189494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005392089,0.0002361833,0.001126309,0.0004705168,0.0002408448,0.0002206858,0.0002119754,0.19788,0.2069664,0.06367057,0.005452336,0.522985],"study_design_scores_gemma":[0.00001527743,0.0000567327,0.0003048174,0.00001554353,0.00005153747,0.0001502838,0.00001280492,0.9721966,0.02001889,0.00487747,0.002279284,0.00002067929],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00636389,0.0001435665,0.9924494,0.0001184892,0.00005425357,0.00001583867,0.00003402252,0.00008685618,0.0007336571],"genre_scores_gemma":[0.1400012,0.0007307723,0.8541991,0.0002207347,0.0001293265,0.0000825496,0.0003074877,0.00009803923,0.004230772],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001661325,"threshold_uncertainty_score":0.004834175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07407870049251018,"score_gpt":0.3722560702875082,"score_spread":0.298177369794998,"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."}}