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Learned Iterative Reconstruction

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

Author

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  • Jonas Adler

    (KTH – Royal Institute of Technology, Department of Mathematics
    Now with DeepMind)

Abstract

Learned iterative reconstruction methods have recently emerged as a powerful tool to solve inverse problems. These deep learning techniques for image reconstruction achieve remarkable speed and accuracy by combining hard knowledge about the physics of the image formation process, represented by the forward operator, with soft knowledge about how the reconstructions should look like, represented by deep neural networks. A diverse set of such methods have been proposed, and this chapter seeks to give an overview of their similarities and differences, as well as discussing some of the commonly used methods to improve their performance.

Suggested Citation

  • Jonas Adler, 2023. "Learned Iterative 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 19, pages 751-771, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98661-2_67
    DOI: 10.1007/978-3-030-98661-2_67
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