Group-based sparse representation for low lighting image enhancement
Wuzhen Shi, Congcong Chen, Feng Jiang, Debin Zhao, Weizheng Shen · 2016
The Group-based Sparse Representation (GSR) is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. And the GSR-driven L0 minimization method for image restoration has been proposed. This paper expands the application of GSR from image restoration to low lighting image enhancement. The GSR is not used to represent the natural images anymore, but representing the transmission map of the haze image and recovering it. Because the transmission map is very important for the low lighting image enhancement, the dark channel prior based enhancement method with the enhanced transmission map can get a better enhanced results. Different from other methods, we evaluate the quality of the enhanced images not only by qualitative analysis but also by quantitative results. Extensive experiments on low lighting image show that the GSR-based method gets a better enhancement result than many current state-of-the-art ones.