A Dual Sensor Computational Camera for High Quality Dark Videography
Yuxiao Cheng, Runzhao Yang, Zhihong Zhang, Jinli Suo, Qionghai Dai

TL;DR
This paper introduces a dual-sensor camera capturing RGB and NIR videos simultaneously, combined with a novel neural network, to significantly improve high-quality dark videography under low-light conditions.
Contribution
It presents a new hardware design of a dual-sensor camera and a dual-channel neural network for reconstructing high-quality videos in extremely dark environments.
Findings
The dual-sensor camera effectively captures more photons in low light.
The DCMAN network improves noise suppression and detail preservation.
Experimental results show superior performance over existing methods.
Abstract
Videos captured under low light conditions suffer from severe noise. A variety of efforts have been devoted to image/video noise suppression and made large progress. However, in extremely dark scenarios, extensive photon starvation would hamper precise noise modeling. Instead, developing an imaging system collecting more photons is a more effective way for high-quality video capture under low illuminations. In this paper, we propose to build a dual-sensor camera to additionally collect the photons in NIR wavelength, and make use of the correlation between RGB and near-infrared (NIR) spectrum to perform high-quality reconstruction from noisy dark video pairs. In hardware, we build a compact dual-sensor camera capturing RGB and NIR videos simultaneously. Computationally, we propose a dual-channel multi-frame attention network (DCMAN) utilizing spatial-temporal-spectral priors to…
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Taxonomy
TopicsCCD and CMOS Imaging Sensors · Infrared Target Detection Methodologies · Image Processing Techniques and Applications
