Low-light image enhancement using CNN and bright channel prior
Tao Li, Chuang Zhu, Jiawen Song, Tao Lu, Huizhu Jia, Xiaodong Xie · 2017
In this paper, we propose a joint framework to enhance images under low-light conditions. First, a convolutional neural network (CNN) based architecture is proposed to denoise low-light images. Then, based on atmosphere scattering model, we introduce a low-light model to enhance image contrast. In our low-light model, we propose a simple but effective image prior, bright channel prior, to estimate the transmission parameter; besides, an effective filter is designed to adaptively estimate environment light in different image areas. Experimental results demonstrate that our method achieves superior performance over other methods.