  Neuromorphic imaging reacts to per-pixel brightness changes of a dynamic
scene with high temporal precision and responds with asynchronous streaming
events as a result. It also often supports a simultaneous output of an
intensity image. Nevertheless, the raw events typically involve a large amount
of noise due to the high sensitivity of the sensor, while capturing fast-moving
objects at low frame rates results in blurry images. These deficiencies
significantly degrade human observation and machine processing. Fortunately,
the two information sources are inherently complementary -- events with
microsecond-level temporal resolution, which are triggered by the edges of
objects recorded in a latent sharp image, can supply rich motion details
missing from the blurry one. In this work, we bring the two types of data
together and introduce a simple yet effective unifying algorithm to jointly
reconstruct blur-free images and noise-robust events in an iterative
coarse-to-fine fashion. Specifically, an event-regularized prior offers precise
high-frequency structures and dynamic features for blind deblurring, while
image gradients serve as a kind of faithful supervision in regulating
neuromorphic noise removal. Comprehensively evaluated on real and synthetic
samples, such a synergy delivers superior reconstruction quality for both
images with severe motion blur and raw event streams with a storm of noise, and
also exhibits greater robustness to challenging realistic scenarios such as
varying levels of illumination, contrast and motion magnitude. Meanwhile, it
can be driven by much fewer events and holds a competitive edge at
computational time overhead, rendering itself preferable as available computing
resources are limited. Our solution gives impetus to the improvement of both
sensing data and paves the way for highly accurate neuromorphic reasoning and
analysis.
