Seeing Motion at Nighttime with an Event Camera
Haoyue Liu, Shihan Peng, Lin Zhu, Yi Chang, Hanyu Zhou, Luxin Yan

TL;DR
This paper introduces a novel event camera-based method for nighttime dynamic scene imaging, leveraging high temporal resolution and dynamic range to overcome limitations of traditional low-light imaging techniques.
Contribution
It proposes NER-Net, a new neural network with modules for event timestamp calibration and illumination awareness, and provides a large paired dataset for nighttime event imaging.
Findings
NER-Net outperforms existing methods in visual quality
The method demonstrates strong generalization on real-world datasets
The dataset RLED includes over 64,000 aligned images and events
Abstract
We focus on a very challenging task: imaging at nighttime dynamic scenes. Most previous methods rely on the low-light enhancement of a conventional RGB camera. However, they would inevitably face a dilemma between the long exposure time of nighttime and the motion blur of dynamic scenes. Event cameras react to dynamic changes with higher temporal resolution (microsecond) and higher dynamic range (120dB), offering an alternative solution. In this work, we present a novel nighttime dynamic imaging method with an event camera. Specifically, we discover that the event at nighttime exhibits temporal trailing characteristics and spatial non-stationary distribution. Consequently, we propose a nighttime event reconstruction network (NER-Net) which mainly includes a learnable event timestamps calibration module (LETC) to align the temporal trailing events and a non-uniform illumination aware…
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Taxonomy
TopicsDark Matter and Cosmic Phenomena · Atomic and Subatomic Physics Research
MethodsGoal-Driven Tree-Structured Neural Model · Attentive Walk-Aggregating Graph Neural Network · ALIGN · Focus
