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Deep quantification down-plain-upsampling residual learning for single image super-resolution

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Abstract

Deep convolutional neural networks have been widely used in single image super-resolution (SISR) with great success. However, the performance and efficiency of such models need to be improved for practical applications. In this paper, a novel deep quantification down-plain-upsampling (QDPU) network for SISR is proposed. In the framework, a down-plain-upsampling (DPU) residual block based on U-Net is firstly designed to reduce the computational cost by transforming the spatial scale of feature maps without sacrificing the reconstruction performance. Then, to better transmit low-level features to the reconstruction layer, we construct quantification skip-connection modules to integrate the outputs of the DPU residual blocks. Finally, QDPU is developed by stacking the DPU residual blocks with multiple skip-connections to reconstruct high-resolution images and reduce the computational burden. Quantitative and qualitative evaluations of the reconstruction results on four benchmark datasets show that the proposed method can achieve better performance compared with several state-of-the-art SISR methods.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 61862030, No. 61662026, and No. 61462031), by the Natural Science Foundation of Jiangxi Province (No. 20182BCB22006, No. 20181BAB202010, No. 20192ACB20002, and No. 20192ACBL21008), and by the Project of the Education Department of Jiangxi Province (No. KJLD14031, No. GJJ170312, and No. GJJ170318).

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Authors and Affiliations

  1. School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Shuying Huang, Haijun Zhu & Yingjun Tang

  2. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330032, China

    Yong Yang & Yifan Zuo

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  1. Shuying Huang
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Correspondence to Yong Yang.

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Huang, S., Zhu, H., Yang, Y. et al. Deep quantification down-plain-upsampling residual learning for single image super-resolution. Int. J. Mach. Learn. & Cyber. 11, 1923–1937 (2020). https://doi.org/10.1007/s13042-020-01083-w

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