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A lightweight generative adversarial network for single image super-resolution

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Abstract

Single image super-resolution is a digital image processing technique that can obtain a corresponding high-resolution image from a low-resolution image. The growth of deep convolutional neural networks in the field of computer vision has greatly benefited recent research on super-resolution. However, the convolutional neural networks often have a large number of parameters, which increases the model’s computational cost and limits its application in practical situations. In order to solve the problem, we propose a lightweight generative adversarial network model using the inception block. According to extensive experimental results on image super-resolution using four widely used datasets, our model not only achieves high scores on the peak signal to noise ratio/structural similarity index matrix, but also enables faster computation compared to other image super-resolution models.

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Data availability

The dataset used in this paper can be downloaded from https://paperswithcode.com/paper/single-image-super-resolution-from.

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Authors and Affiliations

  1. School of Energy and Electrical Engineering, Hohai University, Nanjing, 211100, People’s Republic of China

    Xinbiao Lu, Xupeng Xie, Chunlin Ye, Hao Xing & Zecheng Liu

  2. Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Nanjing, 211100, People’s Republic of China

    Xinbiao Lu & Changchun Cai

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  1. Xinbiao Lu
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  2. Xupeng Xie
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Correspondence to Xupeng Xie.

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Lu, X., Xie, X., Ye, C. et al. A lightweight generative adversarial network for single image super-resolution. Vis Comput 40, 41–52 (2024). https://doi.org/10.1007/s00371-022-02764-z

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