Near-infrared Image Deblurring and Event Denoising with Synergistic Neuromorphic Imaging
This work addresses imaging challenges in nighttime and dark conditions for applications like surveillance or autonomous systems, representing an incremental improvement by integrating existing modalities.
The paper tackled the problem of low-light imaging by developing a synergistic neuromorphic imaging framework that combines near-infrared (NIR) and event cameras to jointly achieve NIR image deblurring and event denoising, demonstrating better accuracy and robustness in experiments on real and simulated sequences.
The fields of imaging in the nighttime dynamic and other extremely dark conditions have seen impressive and transformative advancements in recent years, partly driven by the rise of novel sensing approaches, e.g., near-infrared (NIR) cameras with high sensitivity and event cameras with minimal blur. However, inappropriate exposure ratios of near-infrared cameras make them susceptible to distortion and blur. Event cameras are also highly sensitive to weak signals at night yet prone to interference, often generating substantial noise and significantly degrading observations and analysis. Herein, we develop a new framework for low-light imaging combined with NIR imaging and event-based techniques, named synergistic neuromorphic imaging, which can jointly achieve NIR image deblurring and event denoising. Harnessing cross-modal features of NIR images and visible events via spectral consistency and higher-order interaction, the NIR images and events are simultaneously fused, enhanced, and bootstrapped. Experiments on real and realistically simulated sequences demonstrate the effectiveness of our method and indicate better accuracy and robustness than other methods in practical scenarios. This study gives impetus to enhance both NIR images and events, which paves the way for high-fidelity low-light imaging and neuromorphic reasoning.