{"id":"W4309349667","doi":"10.1007/s10462-022-10305-2","title":"Image denoising in the deep learning era","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence Review","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":93,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Noise reduction; Artificial intelligence; Deep learning; Benchmark (surveying); Noise (video); Machine learning; Deep neural networks; Artificial neural network; Image (mathematics); Image denoising; Pattern recognition (psychology); Computer vision; Data science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001511539,0.00056441,0.0008319693,0.0008279281,0.0001724942,0.001043158,0.0007363222,0.001402884,0.001344028],"category_scores_gemma":[0.002698422,0.0002786169,0.000378841,0.0009815926,0.001321885,0.00191018,0.0007728898,0.002909286,0.00064822],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00080669,"about_ca_system_score_gemma":0.0008414841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001682859,"about_ca_topic_score_gemma":0.001963834,"domain_scores_codex":[0.999678,0.00006525301,0.00002307609,0.00005422759,0.0001575085,0.00002191388],"domain_scores_gemma":[0.998667,0.0007514051,0.00006237315,0.00006040042,0.000410849,0.00004799788],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008471918,0.00006809917,0.0005340814,0.004028433,0.000139177,0.00008645812,0.00006994864,0.009164535,0.00545022,0.08613352,0.01954309,0.8746977],"study_design_scores_gemma":[0.00003252829,0.000233974,0.002373041,0.003394302,0.0001937435,0.001320341,0.0001240566,0.07801525,0.01835446,0.2102661,0.685598,0.00009423816],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003522795,0.8407949,0.1389706,0.008288662,0.001605776,0.00001409222,0.00006484845,0.0001138374,0.006624429],"genre_scores_gemma":[0.04781393,0.8819962,0.05326096,0.003965044,0.003807327,0.00003302039,0.0001217894,0.00009124049,0.008910503],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.001682859,"threshold_uncertainty_score":0.007993877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07066406562607672,"score_gpt":0.3549113393011013,"score_spread":0.2842472736750246,"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."}}