Multimode fibers (MMFs) have the potential to carry complex images for
endoscopy and related applications, but decoding the complex speckle patterns
produced by mode-mixing and modal dispersion in MMFs is a serious challenge.
Several groups have recently shown that convolutional neural networks (CNNs)
can be trained to perform high-fidelity MMF image reconstruction. We find that
a considerably simpler neural network architecture, the single hidden layer
dense neural network, performs at least as well as previously-used CNNs in
terms of image reconstruction fidelity, and is superior in terms of training
time and computing resources required. The trained networks can accurately
reconstruct MMF images collected over a week after the cessation of the
training set, with the dense network performing as well as the CNN over the
entire period.Comment: 17 pages, 10 figure
