{"id":"W3034863454","doi":"10.1109/tip.2020.2999209","title":"Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Google","keywords":"Video denoising; Pixel; Noise reduction; Artificial intelligence; Computer science; Computer vision; Non-local means; Regularization (linguistics); Total variation denoising; Pattern recognition (psychology); Image restoration; Image denoising; Image (mathematics); Image processing; Video processing; Video tracking","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":[0.001179443,0.0006315747,0.0006547931,0.000563232,0.0002845912,0.0005501256,0.001097363,0.001013991,0.0009464871],"category_scores_gemma":[0.002180628,0.0003948644,0.0006640386,0.0006236659,0.0007327599,0.001211016,0.0009892022,0.001546237,0.0003292465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006031817,"about_ca_system_score_gemma":0.0006130957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003685812,"about_ca_topic_score_gemma":0.008162578,"domain_scores_codex":[0.9996769,0.00006146308,0.00001705476,0.00009592239,0.0001119919,0.00003659113],"domain_scores_gemma":[0.9995275,0.0001675887,0.00008357043,0.00007960427,0.0001108827,0.00003085696],"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":[0.0002310605,0.0001358524,0.002186089,0.0001299138,0.0001177545,0.000124085,0.0001448048,0.6473678,0.04643997,0.02257183,0.002555349,0.2779955],"study_design_scores_gemma":[0.000002562118,0.00001508141,0.0001750008,0.000003631287,0.000009183351,0.00002787391,0.000004975628,0.9926074,0.003947799,0.002717795,0.0004843619,0.000004383365],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01360056,0.0002732898,0.9849805,0.0001133211,0.00002608355,0.00001414212,0.00002877101,0.0003158611,0.0006474491],"genre_scores_gemma":[0.473947,0.0007582388,0.5197381,0.00022924,0.000100653,0.00007784005,0.000241736,0.0001884355,0.004718593],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003685812,"threshold_uncertainty_score":0.007328689,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02356500177631189,"score_gpt":0.2814389139877679,"score_spread":0.2578739122114561,"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."}}