Automatic Perception Enhancement for Simulated Retinal Implants

This work addresses the automatic enhancement of visual percepts of virtual patients with retinal implants. Specifically, we render the task as an image transformation problem within an artificial neural network. The neurophysiological model of (Nanduri et al., 2012) was implemented as a tensor network to simulate a virtual patient’s visual percept and used together with an image transformation network in order to perform end-to-end learning on an image reconstruction and a classification task. The image reconstruction task was evaluated using the MNIST data set and yielded plausible results w.r.t. the learned transformations while halving the dissimilarity (mean-squared-error) of an input image to its simulated visual percept. Furthermore, the classification task was evaluated on the cifar-10 data set. Experiments show, that classification accuracy increases by approximately 12.9% when a suitable input image transformation is learned.