  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, a deep learning approach that generalizes over
mechanical perturbations is presented. Using this approach, successful
reconstruction of the input images from intensity-only measurements of speckle
patterns at the output of a 1.5 meter-long randomly perturbed multimode fiber
is demonstrated. The model's success is explained by hidden correlations in the
speckle of random fiber conformations.
