LAUSR

The lighting environment in the real world is so complex that most existing low-light image restoration methods suffer from color cast and local over-exposure. In order to solve these problems, this paper proposes the enhancement-fusion iterative network (EFINet) for low-light image enhancement. Within each iteration of EFINet, a stretching coefficient estimation network based enhancement module is designed to adjust the input image pixel-wisely to obtain the initial enhancement result with the estimated coefficient maps. Then, an encoder-decoder based fusion network is devised to extract the deep features and combine the well-exposed local areas in both the input image and the initially enhanced image, to obtain a visually pleasing, high-quality image enhancement result. The coefficient estimation network and the fusion network are weight shared among all iterations. What’s more, most of the low-light image datasets are generated through illumination reduction in a global way. To better simulate the diverse illumination distribution in the real world, we put forward a new low-light image synthesis method to produce the low-light images with non-uniform illumination for the network training purpose. After conducting extensive experiments on both synthetic and real-world low-light images, the results verify the superiority of our algorithm over the state-of-the-art (SOTA) methods, especially in balancing the brightness difference and preventing over-enhancement.