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.
@article{arxiv.2201.08996,
title = {Linear Array Network for Low-light Image Enhancement},
author = {Keqi Wang and Ziteng Cui and Jieru Jia and Hao Xu and Ge Wu and Yin Zhuang and Lu Chen and Zhiguo Hu and Yuhua Qian},
journal= {arXiv preprint arXiv:2201.08996},
year = {2022}
}