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5 March 2021 Seeing the trees through the forest: training generalizable denoising neural networks using simulated data
Eddie M. Gil, Zachary N. Coker, Vsevolod Cheburkanov, Joel N. Bixler, Vladislav V. Yakovlev
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Proceedings Volume 11622, Multiscale Imaging and Spectroscopy II; 1162204 (2021) https://doi.org/10.1117/12.2582380
Event: SPIE BiOS, 2021, Online Only
Abstract
Deep Networks trained on one kind of data tend to perform poorly, on data that is beyond its training set. We believe this is because data sets tend to focus too directly on a specific task. We circumvent this by simulating various sinusoidal signal sums, with and without envelopes, along with blurred spike trains. We then add various noise to these signals during training to allow the networks to learn a denoising technique. Without using any real Raman or Brillouin data, our network successfully denoises and removes low frequency drifts from real experimentally acquired Raman and Brillouin data.
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Eddie M. Gil, Zachary N. Coker, Vsevolod Cheburkanov, Joel N. Bixler, and Vladislav V. Yakovlev "Seeing the trees through the forest: training generalizable denoising neural networks using simulated data", Proc. SPIE 11622, Multiscale Imaging and Spectroscopy II, 1162204 (5 March 2021); https://doi.org/10.1117/12.2582380
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KEYWORDS
Denoising
Neural networks
Image processing
Image segmentation
Interference (communication)
Inverse optics
Inverse problems
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