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Medical image super-resolution via deep residual neural network in the shearlet domain

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Abstract

This paper proposes aconvolutional neural network (CNN)-based efficient medical image super-resolution (SR) method in the shearlet domain. Because of differences between imaging mechanisms optimized for natural images and medical images, the design begins with building a medical image dataset for medical image SR and extracting effective areas to remarkably enhance the training effects of the CNN-based method. Then, a new medical image SR network structure—deep medical super-resolution network (DMSRN)—has been designed in which local residual learning is implemented through a recursive network and combined with global residual learning to heighten the depth of the network on the ground with no parameter increase. This effectively fixes the long-term dependency problem, which causes the prior state layers to barely have any effect on the following state layers. Last, the design addresses the problem of too-smooth reconstruction effects in the CNN-based method in the image space domain; shearlet transform is introduced to DMSRN to restore global topology through low-frequency sub-bands and restore local edge detail information through high-frequency sub-bands. Experimental results show that the proposed method is better than other state-of-the-art methods for medical image SR, which significantly promotes the restoration ability of texture structure and edge details.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos: 61802212 and 61872203), the Shandong Provincial Natural Science Foundation (No: ZR2019BF017), the Project of Shandong Province Higher Educational Science and Technology Program (No: J18KA331), China Postdoctoral Science Foundation (No: 2020M670728), Major Scientific and Technological Innovation Projects of Shandong Province (Nos: 2019JZZY010127, 2019JZZY010132, and 2019JZZY010201), Plan of Youth Innovation Team Development of Colleges and Universities in Shandong Province (No: SD2019-161) and Jinan City 20 universities Funding Projects Introducing Innovation Team Program (No: 2019GXRC031).

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Authors and Affiliations

  1. School of Cyber Security, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China

    Chunpeng Wang, Zhiqiu Xia, Qi Li, Bin Ma, Jian Li & Meihong Yang

  2. Shandong Provincial Key Laboratory of Computer Networks, Qilu University of Technology (Shandong Academy of Sciences), Shandong Computer Science Center (National Supercomputer Center in Jinan), Jinan, 250014, China

    Chunpeng Wang, Qi Li, Jian Li & Meihong Yang

  3. School of Electronic and Information Engineering, Liaoning Technical University, Huludao, 125105, China

    Simiao Wang

  4. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA

    Yun-Qing Shi

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  1. Chunpeng Wang
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Correspondence to Bin Ma.

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Wang, C., Wang, S., Xia, Z. et al. Medical image super-resolution via deep residual neural network in the shearlet domain. Multimed Tools Appl 80, 26637–26655 (2021). https://doi.org/10.1007/s11042-021-10894-0

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