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Volume 8 Issue 7
Jul.  2021

IEEE/CAA Journal of Automatica Sinica

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Long Sun, Zhenbing Liu, Xiyan Sun, Licheng Liu, Rushi Lan and Xiaonan Luo, "Lightweight Image Super-Resolution via Weighted Multi-Scale Residual Network," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1271-1280, July 2021. doi: 10.1109/JAS.2021.1004009
Citation: Long Sun, Zhenbing Liu, Xiyan Sun, Licheng Liu, Rushi Lan and Xiaonan Luo, "Lightweight Image Super-Resolution via Weighted Multi-Scale Residual Network," IEEE/CAA J. Autom. Sinica, vol. 8, no. 7, pp. 1271-1280, July 2021. doi: 10.1109/JAS.2021.1004009

Lightweight Image Super-Resolution via Weighted Multi-Scale Residual Network

doi: 10.1109/JAS.2021.1004009
Funds:  This work was supported in part by the National Natural Science Foundation of China (61772149, 61866009, 61762028, U1701267, 61702169), Guangxi Science and Technology Project (2019GXNSFFA245014, ZY20198016, AD18281079, AD18216004), the Natural Science Foundation of Hunan Province (2020JJ3014), and Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics (GIIP202001)
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  • The tradeoff between efficiency and model size of the convolutional neural network (CNN) is an essential issue for applications of CNN-based algorithms to diverse real-world tasks. Although deep learning-based methods have achieved significant improvements in image super-resolution (SR), current CNN-based techniques mainly contain massive parameters and a high computational complexity, limiting their practical applications. In this paper, we present a fast and lightweight framework, named weighted multi-scale residual network (WMRN), for a better tradeoff between SR performance and computational efficiency. With the modified residual structure, depthwise separable convolutions (DS Convs) are employed to improve convolutional operations’ efficiency. Furthermore, several weighted multi-scale residual blocks (WMRBs) are stacked to enhance the multi-scale representation capability. In the reconstruction subnetwork, a group of Conv layers are introduced to filter feature maps to reconstruct the final high-quality image. Extensive experiments were conducted to evaluate the proposed model, and the comparative results with several state-of-the-art algorithms demonstrate the effectiveness of WMRN.

     

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    Highlights

    • A novel weighted multi-scale residual block (WMRB) is proposed, which can not only effectively exploit multi-scale features but also dramatically reduce the computational burden.
    • A global residual shortcut is deployed, which adds high frequency features to generate more clear details and promote gradient information propagation.
    • Extensive experiments show that the WMRN model utilizes only a modest number of parameters and operations to achieve competitive SR performance on different benchmarks with different upscaling factors.

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