{"id":"W3014708648","doi":"10.1109/tcsi.2020.2981387","title":"Real-Time Light Field Denoising Using a Novel Linear 4-D Hyperfan Filter","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems I Regular Papers","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"University of Moratuwa; University of New South Wales; Xilinx","keywords":"Grayscale; Noise reduction; Computer science; Field-programmable gate array; Preprocessor; Artificial intelligence; Filter (signal processing); Peak signal-to-noise ratio; Noise (video); Computer vision; Pattern recognition (psychology); Computer hardware; 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":[0.0004261032,0.0004716438,0.0003822196,0.0006619135,0.0002770374,0.0005698742,0.000647522,0.0007284657,0.002255589],"category_scores_gemma":[0.0006497548,0.0002464376,0.0005643035,0.0004483549,0.0003780721,0.000808659,0.0005085786,0.0004739636,0.0006510709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006096796,"about_ca_system_score_gemma":0.0006419298,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001540772,"about_ca_topic_score_gemma":0.00178726,"domain_scores_codex":[0.999772,0.0000400405,0.00001195096,0.00004429459,0.0001123909,0.00001931109],"domain_scores_gemma":[0.9996735,0.0001167065,0.00004406231,0.00003969768,0.0001014875,0.00002446942],"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":[0.0004575816,0.0001378545,0.001158441,0.0002400307,0.00007251881,0.0002981276,0.0002331784,0.07017908,0.5387928,0.01321909,0.002012214,0.373199],"study_design_scores_gemma":[0.00003791585,0.0002624333,0.0006958898,0.00003231115,0.00003025011,0.0005175556,0.00003878573,0.8148876,0.1698144,0.00226596,0.01135323,0.00006359006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02669006,0.0002799073,0.9704792,0.0001491723,0.00004307082,0.0000408533,0.00004016764,0.0005185708,0.001758998],"genre_scores_gemma":[0.1645423,0.0005533172,0.829591,0.0001417967,0.00005009544,0.0001122972,0.0001440091,0.0000674872,0.004797699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002255589,"threshold_uncertainty_score":0.00754571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02588845739554335,"score_gpt":0.2206784083864183,"score_spread":0.1947899509908749,"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."}}