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Pixel attention convolutional network for image super-resolution

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Abstract

We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Single-image super-resolution reconstruction technology is to reconstruct fuzzy low-resolution images into clearer high-resolution images. It is a research hotspot in the field of computer vision and image processing. In recent years, the attention mechanism has been successfully applied in image super-resolution reconstruction. However, the existing methods use the channel attention mechanism and the spatial attention mechanism separately, or simply superimpose them, which cannot effectively unify the adjustment effects of both, and the performance is limited. This paper proposes a method that can merge channel attention and spatial attention into pixel attention, which achieves more precise adjustment of feature map information. The pixel attention convolutional neural network method built on this basis can improve the quality of image texture detail reconstruction. We have been tested on five widely used standard datasets, the experimental results show that the method is superior to most current representative reconstruction methods, especially in terms of high-definition picture texture restoration.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Nos.61901099 and 61876205)

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Authors and Affiliations

  1. School of Computer Science and Engineering, Northeastern University, Shenyang, China

    Shufen Zhang & Jiale Zhang

  2. School of Computer and Communication Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China

    Xin Wang, Yuanyuan Lin & Yanxia Lyu

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  1. Xin Wang
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  2. Shufen Zhang
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  5. Jiale Zhang
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Correspondence to Yanxia Lyu.

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Wang, X., Zhang, S., Lin, Y. et al. Pixel attention convolutional network for image super-resolution. Neural Comput & Applic 35, 8589–8599 (2023). https://doi.org/10.1007/s00521-022-08132-1

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