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Chest X-ray images super-resolution reconstruction via recursive neural network

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Abstract

To address the problems of insufficient detail extraction and long training time in the super-resolution reconstruction of chest X-ray images, a method of chest X-ray images super-resolution reconstruction using recursive neural network is proposed in this paper. Firstly, this paper designs a lightweight recursive network as the main branch, which solves the problem of training difficulty and time-consuming. Then, to overcome the lack of detail extraction in chest X-ray image, a detail complementary model is designed as another branch of the network to solve the problem of shallow information loss. Finally, the optimized activation function is used to reduce the loss of texture details and make the reconstructed image more complete and richer. When the scale factor is 2, the experimental results show that compared with other methods based on deep learning, such as the deep recursive neural network (DRCN), the details of chest X-ray images reconstructed by our method are more abundant. Specifically, the average value of PSNR and SSIM were improved by 0.17 dB and 0.0013 respectively. Moreover, the reconstruction speed of the images was increased by about 16% compared with DRCN.

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Acknowledgements

The authors are grateful for collaborative funding support from the Natural Science Foundation of Shandong Province, China (ZR2018MEE008), the Key Research and Development Program of Shandong Province, China (2017GSF20115).

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Authors and Affiliations

  1. College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Chao-Yue Zhao, Rui-Sheng Jia, Qing-Ming Liu, Xiao-Ying Liu, Hong-Mei Sun & Xing-Li Zhang

  2. Shandong Province Key Laboratory of Wisdom Mine Information Technology, Shandong University of Science and Technology, Qingdao, 266590, China

    Rui-Sheng Jia, Hong-Mei Sun & Xing-Li Zhang

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  1. Chao-Yue Zhao
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  2. Rui-Sheng Jia
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Correspondence to Rui-Sheng Jia or Hong-Mei Sun.

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Zhao, CY., Jia, RS., Liu, QM. et al. Chest X-ray images super-resolution reconstruction via recursive neural network. Multimed Tools Appl 80, 263–277 (2021). https://doi.org/10.1007/s11042-020-09773-x

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  • DOI: https://doi.org/10.1007/s11042-020-09773-x

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