  Convolution neural networks (CNNs) based methods have dominated the low-light
image enhancement tasks due to their outstanding performance. However, the
convolution operation is based on a local sliding window mechanism, which is
difficult to construct the long-range dependencies of the feature maps.
Meanwhile, the self-attention based global relationship aggregation methods
have been widely used in computer vision, but these methods are difficult to
handle high-resolution images because of the high computational complexity. To
solve this problem, this paper proposes a Linear Array Self-attention (LASA)
mechanism, which uses only two 2-D feature encodings to construct 3-D global
weights and then refines feature maps generated by convolution layers. Based on
LASA, Linear Array Network (LAN) is proposed, which is superior to the existing
state-of-the-art (SOTA) methods in both RGB and RAW based low-light enhancement
tasks with a smaller amount of parameters. The code is released in
https://github.com/cuiziteng/LASA_enhancement.
