Low-Light Image Enhancement via Progressive-Recursive Network

Low-light images have low brightness and contrast, which presents a huge obstacle to computer vision tasks. Low-light image enhancement is challenging because multiple factors (such as brightness, contrast, artifacts, and noise) must be considered simultaneously. In this study, we propose a neural network—a progressive-recursive image enhancement network (PRIEN)—to enhance low-light images. The main idea is to use a recursive unit, composed of a recursive layer and a residual block, to repeatedly unfold the input image for feature extraction. Unlike in previous methods, in the proposed study, we directly input low-light images into the dual attention model for global feature extraction. Next, we use a combination of recurrent layers and residual blocks for local feature extraction. Finally, we output the enhanced image. Furthermore, we input the global feature map of dual attention into each stage in a progressive way. In the local feature extraction module, a recurrent layer shares depth features across stages. In addition, we perform recursive operations on a single residual block, significantly reducing the number of parameters while ensuring good network performance. Although the network structure is simple, it can produce good results for a range of low-light conditions. We conducted experiments on widely adopted datasets. The results demonstrate the advantages of our method compared with other methods, from both qualitative and quantitative perspectives.