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Single Image Super-Resolution for Medical Image Applications

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Pattern Recognition and Artificial Intelligence (ICPRAI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12068))

Abstract

In medical imaging, high-resolution images are expected to have the ability to deliver a more precise diagnosis with the practical application of high-resolution displays. This research proposes a deep learning method for single image super-resolution that learns an end-to-end mapping between the low and high-resolution images. It redesigns the SRGAN, using VGG19 network for feature extraction, setting discriminator network’s working space as feature space, and adding the loss function based on the mean square error of pixel space, gaining more details by incorporating SRCNN layers to increase the PSNR in the reconstruction at the same time. To thoroughly investigate the system, we compared the performance with other architectures on MNIST and CIFAR-10 dataset with a further evaluation conducted on Chest x-ray.

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Author information

Authors and Affiliations

  1. CENPARMI, Concordia University, Montreal, QC, Canada

    Tamarafinide V. Dittimi & Ching Y. Suen

Authors
  1. Tamarafinide V. Dittimi
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  2. Ching Y. Suen
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Corresponding author

Correspondence to Tamarafinide V. Dittimi .

Editor information

Editors and Affiliations

  1. East China Normal University, Shanghai, China

    Yue Lu

  2. Paris Descartes University, Paris, France

    Nicole Vincent

  3. Hong Kong Baptist University, Kowloon, Hong Kong

    Pong Chi Yuen

  4. Sun Yat-sen University, Guangzhou, China

    Wei-Shi Zheng

  5. Polytechnique Montréal, Montreal, QC, Canada

    Farida Cheriet

  6. Concordia University, Montreal, QC, Canada

    Ching Y. Suen

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Dittimi, T.V., Suen, C.Y. (2020). Single Image Super-Resolution for Medical Image Applications. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_57

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  • DOI: https://doi.org/10.1007/978-3-030-59830-3_57

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-59829-7

  • Online ISBN: 978-3-030-59830-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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