Fast Imaging in the Dark

Pixart and National Chiao Tung University, Taiwan publish an open access paper "Fast Imaging in the Dark by using Convolutional Network" by Mian Jhong Chiu, Guo-Zhen Wang, and Jen-Hui Chuang presented at 2019 IEEE International Symposium on Circuits and Systems (ISCAS):

"While fast imaging in low-light condition is crucial for surveillance and robot applications, it is still a formidable challenge to resolve the seemingly inevitable high noise level and low photon count issues. A variety of image enhancement methods such as de-blurring and de-noising have been proposed in the past. However, limitations can still be found in these methods under extreme low-light condition. To overcome such difficulty, a learning-based image enhancement approach is proposed in this paper. In order to support the development of learning-based methodology, we collected a new low-lighting dataset (less than 0.1 lux) of raw short-exposure (6.67 ms) images, as well as the corresponding long-exposure reference images. Based on such dataset, we develop a light-weight convolutional network structure which is involved with fewer parameters and has lower computation cost compared with a regular-size network. The presented work is expected to make possible the implementation of more advanced edge devices, and their applications."