Low-Light Image Enhancement via Gradient Prior-Aided Network

Low-light images have low brightness and low contrast, which brings huge obstacles to the intelligent video surveillance system. The enhancement of low-light images must simultaneously consider the interference of factors such as brightness, contrast, artifacts, and noise. To this end, in this study, we propose a gradient prior-aided low-light enhancement network (GPANet). The main idea is to improve the network's ability to extract edge features and remove unwanted noise by introducing first-order (i.e., Sobel Filter) and second-order gradient (i.e., Laplacian Filter) features. Unlike in previous methods, in the proposed study, we first extract the first-order and second-order gradient information of low-light images and concatenate them with low-light images for multi-view feature analysis in the multi-view fusion encoder (MFE). Then, we suggest the multi-branch topology module (MTM) to fuse and decompose the multi-view features. Finally, we reconstruct the multi-view features through multi-view decomposition decoders (MDDs, including three sub-decoders) to generate potentially normal-light images. The first- and second-order gradient decoders will provide the enhancement decoder with multi-scale gradient prior features. Furthermore, we suggest a residual network to speed up network convergence while ensuring stable enhancement performance. We conduct experiments on widely adopted datasets. The results demonstrate the advantages of our method compared to other methods from both qualitative and quantitative perspectives. The source code is available at https://github.com/LouisYuxuLu/GPANet.