vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff6f90310000007321000001001400
Images captured in low-light conditions usually suffer from very low contrast, which increases the difficulty of computer vision tasks in a great extent. Existing low-light image restoration methods still have limitation in image naturalness and noise. In this paper, we propose an efficient deep residual network that learns difference map between low-light image and original image and restores the low-light image. Additionally, we propose a new low-light image generator, which is used to train the deep residual network. Especially the proposed generator can simulate low-light images containing luminance sources and completely darkness parts. Our experiments demonstrate that the proposed method achieves good results for both synthetic and natural low-light images.