Event-based High Dynamic Range Image and Very High Frame Rate VideoGeneration using Conditional Generative Adversarial Networks
Abstract
Event cameras have a lot of advantages over traditional
cameras, such as low latency, high temporal resolution, and
high dynamic range. However, since the outputs of event
cameras are the sequences of asynchronous events over
time rather than actual intensity images, existing algorithms
could not be directly applied. Therefore, it is demanding
to generate intensity images from events for other tasks. In
this paper, we unlock the potential of event camera-based
conditional generative adversarial networks to create images/videos from an adjustable portion of the event data
stream. The stacks of space-time coordinates of events are
used as inputs and the network is trained to reproduce images based on the spatio-temporal intensity changes. The
usefulness of event cameras to generate high dynamic range
(HDR) images even in extreme illumination conditions and
also non blurred images under rapid motion is also shown.
In addition, the possibility of generating very high frame
rate videos is demonstrated, theoretically up to 1 million
frames per second (FPS) since the temporal resolution of
event cameras are about 1 µs. Proposed methods are evaluated by comparing the results with the intensity images
captured on the same pixel grid-line of events using online
available real datasets and synthetic datasets produced by
the event camera simulator.