论文类型：期刊论文
第一作者：YANHUI GUO
通讯作者：马杰
合写作者：XUE KE,张钧
发表刊物：IEEE Access
学科门类：工学
一级学科：控制科学与工程
文献类型：J
卷号：7
页面范围：13737-13744
关键字：Convolutional neural network, low-light image enhancement, LLIE-Net
DOI码：10.1109/ACCESS.2019.2891957
发表时间：2019-01-10
摘要：Low-light image enhancement is an important challenge in computer vision. Most of the low-light images taken in low-light conditions usually look noisy and dark, which makes it more difficult for subsequent computer vision tasks. In this paper, inspired by multi-scale retinex, we present a low-light image enhancement pipeline network based on an end-to-end fully convolutional networks and discrete wavelet transformation (DWT). First, we show that multiscale retinex (MSR) can be considered as a convolutional neural network with Gaussian convolution kernel, and blending the result of DWT can improve the image produced by MSR. Second, we propose our pipeline neural network, consisting of denoising net and low-light image enhancement net, which learns a function from a pair of dark and bright images. Finally, we evaluate our method both in the synthetic dataset and public dataset. The experiments reveal that in comparison with other state-of-the-art methods, our methods achieve a better performance in the perspective of qualitative and quantitative analyses.