Linear Array Network for Low-light Image Enhancement
Keqi Wang, Ziteng Cui, Jieru Jia, Hao Xu, Ge Wu, Yin Zhuang, Lu Chen,, Zhiguo Hu, Yuhua Qian

TL;DR
This paper introduces a Linear Array Self-attention mechanism and a corresponding network that effectively enhances low-light images by capturing global dependencies with reduced computational complexity, outperforming current state-of-the-art methods.
Contribution
The paper proposes LASA, a novel self-attention mechanism that efficiently models global relationships, and integrates it into LAN, a network that surpasses existing methods in low-light image enhancement.
Findings
LAN outperforms SOTA methods in RGB and RAW low-light enhancement
LAN achieves higher quality with fewer parameters
LASA reduces computational complexity of global feature modeling
Abstract
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…
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Taxonomy
TopicsImage Enhancement Techniques · Advanced Vision and Imaging · Advanced Image Processing Techniques
MethodsConvolution
