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Variational Model-Based Deep Neural Networks for Image Reconstruction

In: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

Author

Listed:
  • Yunmei Chen

    (University of Florida, Department of Mathematics)

  • Xiaojing Ye

    (Georgia State University, Department of Mathematics and Statistics)

  • Qingchao Zhang

    (University of Florida, Department of Mathematics)

Abstract

In recent years, we have witnessed unprecedented growth of research interests in deep learning approaches to image reconstruction. A majority of these approaches are inspired by the well-developed variational method and associated optimization algorithms for the inverse problem of image reconstruction. These approaches mimic the iterative schemes of the standard optimization algorithms but integrate learnable components to form structured deep neural networks and employ large amount of observation data to train the networks for the specific reconstruction tasks. They have demonstrated significantly improved empirical performance and require much lower computational cost compared to the classical methods in a variety of applications. We provide the details of the derivations, the network architectures, and the training procedures for several typical networks in this field.

Suggested Citation

  • Yunmei Chen & Xiaojing Ye & Qingchao Zhang, 2023. "Variational Model-Based Deep Neural Networks for Image Reconstruction," Springer Books, in: Ke Chen & Carola-Bibiane Schönlieb & Xue-Cheng Tai & Laurent Younes (ed.), Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, chapter 23, pages 879-907, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98661-2_57
    DOI: 10.1007/978-3-030-98661-2_57
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