{"id":"W3013538405","doi":"10.1007/978-3-030-59722-1_5","title":"Patch-Based Non-local Bayesian Networks for Blind Confocal Microscopy Denoising","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Noise reduction; Leverage (statistics); Computer science; Pattern recognition (psychology); Posterior probability; Maximum a posteriori estimation; Convolutional neural network; Noise (video); Bayesian probability; Gaussian noise; Shot noise; Gaussian; Machine learning; Computer vision; Mathematics; Statistics; Physics; Image (mathematics)","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":[0.001619181,0.0007353545,0.001315915,0.0007206622,0.0003300293,0.0008685606,0.001735999,0.00178853,0.002306494],"category_scores_gemma":[0.005883221,0.0009742503,0.0009953358,0.0007902706,0.001005313,0.001502751,0.001758677,0.001840858,0.0009131678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008414804,"about_ca_system_score_gemma":0.0009426865,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005472014,"about_ca_topic_score_gemma":0.007331748,"domain_scores_codex":[0.9992962,0.0002403314,0.0000299324,0.0001859485,0.0001915346,0.00005604837],"domain_scores_gemma":[0.9981424,0.001057802,0.0001681464,0.0002246555,0.000339853,0.00006725846],"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":[0.0003737353,0.00008544544,0.0006916591,0.0002626654,0.0001597629,0.00007638549,0.0001295778,0.7095428,0.02205262,0.02901152,0.002745507,0.2348682],"study_design_scores_gemma":[0.000005168504,0.00001313833,0.0001132143,0.000006309748,0.00001156567,0.00002302589,0.000004269735,0.9902411,0.001664883,0.0075264,0.0003835663,0.000007369411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002254308,0.000110217,0.9972256,0.00004753603,0.000006034531,0.000009081931,0.00003070562,0.0001052757,0.0002112177],"genre_scores_gemma":[0.2375036,0.001043002,0.7530252,0.0001905349,0.0001126804,0.000205893,0.0005433491,0.0003412613,0.007034408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005472014,"threshold_uncertainty_score":0.01088029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02560165186904007,"score_gpt":0.3146057455498374,"score_spread":0.2890040936807973,"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."}}