  Dynamic vision sensors are able to operate at high temporal resolutions
within resource constrained environments, though at the expense of capturing
static content. The sparse nature of event streams enables efficient downstream
processing tasks as they are suited for power-efficient spiking neural
networks. One of the challenges associated with neuromorphic vision is the lack
of interpretability of event streams. While most application use-cases do not
intend for the event stream to be visually interpreted by anything other than a
classification network, there is a lost opportunity to integrating these
sensors in spaces that conventional high-speed CMOS sensors cannot go. For
example, biologically invasive sensors such as endoscopes must fit within
stringent power budgets, which do not allow MHz-speeds of image integration.
While dynamic vision sensing can fill this void, the interpretation challenge
remains and will degrade confidence in clinical diagnostics. The use of
generative adversarial networks presents a possible solution to overcoming and
compensating for a vision chip's poor spatial resolution and lack of
interpretability. In this paper, we methodically apply the Pix2Pix network to
naturalize the event stream from spike-converted CIFAR-10 and Linnaeus 5
datasets. The quality of the network is benchmarked by performing image
classification of naturalized event streams, which converges to within 2.81% of
equivalent raw images, and an associated improvement over unprocessed event
streams by 13.19% for the CIFAR-10 and Linnaeus 5 datasets.
