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Gradual deep residual network for super-resolution

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Abstract

Deep neural networks with single upsampling have achieved the improvement of performance for single image super-resolution. However, these networks lose a lot of details of low-resolution image in the reconstruction process. In this paper, we propose a gradual deep residual network for super-resolution (GDSR), which consists of multiple reconstruction network with 2 scale factor (2X reconstruction network). In 2X reconstruction network, a residual block connected by residual (RBR) is proposed to form a deep residual network, which is used to extract the depth features of low-resolution images; then the extracted features are upsampled into the features of high-resolution image by sub-pixel convolutional layer. GDSR gradually reconstructs high-quality high-resoluiton images from low-resolution images by multiple 2X reconstruction networks. Extensive experiments on benchmark datasets demonstrate that the proposed GDSR outperforms the state-of-the-art methods in terms of quantitative evaluation, visual evaluation, and execution time evaluation.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (61763029, 61873116), the Industrial support and guidance project of colleges and universities of Gansu Province (2019C-05), the open fund project of Key Laboratory of Gansu Advanced Control for Industrial Processes (2019KFJJ01) and the open fund project of Key Laboratory of Gansu Advanced Control for Industrial Processes (2019KFJJ01).

The authors would like to thank the editors and reviewers for their valuable comments and suggestions.

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Authors and Affiliations

  1. College of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Zhaoyang Song, Xiaoqiang Zhao & Hongmei Jiang

  2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou, 730050, China

    Xiaoqiang Zhao & Hongmei Jiang

  3. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

    Xiaoqiang Zhao & Hongmei Jiang

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  1. Zhaoyang Song
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  2. Xiaoqiang Zhao
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  3. Hongmei Jiang
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Correspondence to Xiaoqiang Zhao.

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Cite this article

Song, Z., Zhao, X. & Jiang, H. Gradual deep residual network for super-resolution. Multimed Tools Appl 80, 9765–9778 (2021). https://doi.org/10.1007/s11042-020-10152-9

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