  Neuromorphic imaging is a bio-inspired technique that imitates the human
retina to sense variations in a dynamic scene. It responds to pixel-level
brightness changes by asynchronous streaming events and boasts microsecond
temporal precision over a high dynamic range, yielding blur-free recordings
under extreme illumination. Nevertheless, such a modality falls short in
spatial resolution and leads to a low level of visual richness and clarity.
Pursuing hardware upgrades is expensive and might cause compromised performance
due to more burdens on computational requirements. Another option is to harness
offline, plug-in-play neuromorphic super-resolution solutions. However,
existing ones, which demand substantial sample volumes for lengthy training on
massive computing resources, are largely restricted by real data availability
owing to the current imperfect high-resolution devices, as well as the
randomness and variability of motion. To tackle these challenges, we introduce
the first self-supervised neuromorphic super-resolution prototype. It can be
self-adaptive to per input source from any low-resolution camera to estimate an
optimal, high-resolution counterpart of any scale, without the need of side
knowledge and prior training. Evaluated on downstream event-driven tasks, such
a simple yet effective method can obtain competitive results against the
state-of-the-arts, significantly promoting flexibility but not sacrificing
accuracy. It also delivers enhancements for inferior natural images and optical
micrographs acquired under non-ideal imaging conditions, breaking through the
limitations that are challenging to overcome with traditional frame techniques.
In the current landscape where the use of high-resolution cameras for
event-based sensing remains an open debate, our solution serves as a
cost-efficient and practical alternative, paving the way for more intelligent
imaging systems.
