LLCNN: A convolutional neural network for low-light image enhancement
Tao Li, Chuang Zhu, Guoqing Xiang, Yuan Li, Huizhu Jia, Xiaodong Xie · 2017
In this paper, we propose a CNN based method to perform low-light image enhancement. We design a special module to utilize multiscale feature maps, which can avoid gradient vanishing problem as well. In order to preserve image textures as much as possible, we use SSIM loss to train our model. The contrast of low-light images can be adaptively enhanced using our method. Results demonstrate that our CNN based method outperforms other contrast enhancement methods.