SMNet: Synchronous Multi-Scale Low Light Enhancement Network With Local and Global Concern
Shideng Lin, Fan Tang, Weiming Dong, Xingjia Pan, Changsheng Xu
Abstract
Limited by objectively poor lighting conditions and hardware devices, low-light images with low visual quality and low visibility are inevitable in the real world. Accurate local details and reasonable global information play their essential and distinct roles in low-light image enhancement: local details contribute to fine textures, while global information is critical for a proper understanding of the global brightness level. In this paper, we focus on integrating local and global aspects to achieve high-quality low-light image enhancement by proposing the synchronous multi-scale low-light enhancement network (SMNet). A synchronous multi-scale representation learning structure and a global feature recalibration module are adopted in SMNet. Different from the traditional multi-scale feature learning architecture, SMNet carries out the multi-scale representation learning in a synchronous way: we first calculate the rough contextual representations in a top-down manner and then learn multi-scale representations in a bottom-up way to generate representations with rich local details. To acquire global brightness information, a global feature recalibration module (GFRM) is applied after the synchronous multi-scale representations to perceive and exploit proper global information by global pooling and projection to recalibrate channel weights globally. The synchronous multi-scale representation and GFRM compose the basic local-and-global block. Experimental results on mainstream real-world dataset LOL and synthetic dataset MIT-Adobe FiveK show that the proposed SMNet not only leads the way on objective metrics (0.41/2.31 improvement of PSNR on two datasets) but is also superior in subjective comparisons compared with typical SoTA methods. The code had already been uploaded to <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/linshideng/SMNet</uri> .