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Linear Array Network for Low-light Image Enhancement

2022-01-22 08:44:02
Keqi Wang, Ziteng Cui, Ge Wu, Yin Zhuang, Yuhua Qian
arXiv_CV
             
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 based low-light enhancement tasks with a smaller amount of parameters. The code is released in \url{this https URL}.

Abstract (translated)

URL

https://arxiv.org/abs/2201.08996

PDF

https://arxiv.org/pdf/2201.08996.pdf

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