{"id":"W2769290426","doi":"10.1049/cje.2017.09.031","title":"Noise Reduction for Images with Non‐uniform Noise Using Adaptive Block Matching 3D Filtering","year":2017,"lang":"en","type":"article","venue":"Chinese Journal of Electronics","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Noise reduction; Block (permutation group theory); Noise (video); Reduction (mathematics); Computer science; Mathematics; Acoustics; Image denoising; Computer vision; Artificial intelligence; Algorithm; Image (mathematics); Physics; Combinatorics; 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":[0.00108582,0.0002459977,0.0003971193,0.0001894858,0.0007614346,0.0006267524,0.001086405,0.00006521623,0.000001412637],"category_scores_gemma":[0.0001359832,0.0001694231,0.0001732083,0.0001468785,0.00006107608,0.002187374,0.0001421272,0.0004449536,8.50353e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001853352,"about_ca_system_score_gemma":0.0003788324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001947493,"about_ca_topic_score_gemma":0.000005288196,"domain_scores_codex":[0.9985387,0.0000539286,0.0004014819,0.0002376252,0.0003228536,0.0004453867],"domain_scores_gemma":[0.997831,0.00009103348,0.0009316355,0.000538834,0.0004927668,0.0001147392],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001049336,0.0001649982,0.0001496601,0.00009286789,0.0002743559,0.0001672852,0.001939533,0.05129194,0.9028037,0.0005483326,0.0001670046,0.04135099],"study_design_scores_gemma":[0.008420034,0.00436012,0.003833979,0.001092582,0.0003640697,0.01084161,0.0001514493,0.7386187,0.1910534,0.03912374,0.0007059735,0.001434267],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3345499,0.0002600684,0.6643584,0.0001805302,0.0003409373,0.0001091904,0.000001076855,0.00001375056,0.0001860841],"genre_scores_gemma":[0.5660185,0.00004661929,0.4333827,0.00002689055,0.0004087452,0.000001704694,2.824791e-7,0.00002045474,0.00009406633],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7117503,"threshold_uncertainty_score":0.6908873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02083292071826186,"score_gpt":0.3018322698843054,"score_spread":0.2809993491660436,"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."}}