Image transmission through a flexible multimode fiber by deep learning

When multimode optical fibers are perturbed, the data that is transmitted through them is scrambled. This presents a major difficulty for many possible applications, such as multimode fiber based telecommunication and endoscopy. To overcome this challenge we present a deep learning approach that generalizes over mechanical perturbations. Using our approach, we are able to successfully reconstruct the input images from intensity-only measurements of speckle patterns at the output of a 1.5 meter-long randomly perturbed multimode fiber. We explain the model's success by hidden correlations in the speckle of random fiber conformations.