High dynamic range (HDR) imaging is a technique that allows an extensive dynamic range
of exposures, which is important in image processing, computer graphics, and computer …

The best performing learning algorithms devised for event cameras work by first converting
events into dense representations that are then processed using standard CNNs. However …

Neuromorphic vision sensor is a new bio-inspired imaging paradigm that reports
asynchronous, continuously per-pixel brightness changes called'events' with high temporal …

Event-based motion deblurring has shown promising results by exploiting low-latency
events. However, current approaches are limited in their practical usage, as they assume the …

Traditional frame-based video frame interpolation (VFI) methods rely on the linear motion
assumption and brightness invariance assumption, which may lead to fatal errors …

Spiking neural networks (SNNs) have recently demonstrated outstanding performance in a
variety of high-level tasks, such as image classification. However, advancements in the field …

State-of-the-art machine-learning methods for event cameras treat events as dense
representations and process them with conventional deep neural networks. Thus, they fail to …

Existing methods for event stream super-resolution (SR) either require high-quality and high-
resolution frames or underperform for large factor SR. To address these problems, we …

Event cameras have emerged as a promising vision sensor in recent years due to their
unparalleled temporal resolution and dynamic range. While registration of 2D RGB images …

Although synthetic aperture imaging (SAI) can achieve the seeing-through effect by blurring
out off-focus foreground occlusions while recovering in-focus occluded scenes from multi …