SARN: A Lightweight Stacked Attention Residual Network for Low-Light Image Enhancement

Abstract

Low-light Image suffers from low contrast and brightness. If we increase the brightness of the image, the noise hidden in the dark regions will be amplified, and color and detail information may be lost after brightness enhancement. In this paper, we propose a lightweight Stacked Attention Residual Network (denoted as SARN) for low-light image enhancement. We insert Channel Attention Module (SE Module) into the residual block and its shortcut to construct the Attention Residual Block (ARB) for noise removal, and then stack ARBs as the backbone of our SARN. We insert Bottleneck Attention Module (BAM Module) into the bottlenecks to specially deal with the severe noise in real-world images. We extract the shallow features of the low-light images first, and then fuse the extracted shallow features with the high-level output features of the backbone through global skip connection to preserve the color information. Extensive ablation and comparative experiments demonstrate that our method outperforms many other state-of-the-art methods with much less time cost.