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Learned Regularizers for Inverse Problems

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

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  • Sebastian Lunz

    (University of Cambridge, Department of Applied Mathematics and Theoretical Physics)

Abstract

In the past years, there has been a surge of interest in methods to solve inverse problems that are based on neural networks and deep learning. A variety of approaches have been proposed, showing improvements in reconstruction quality over existing methods. Among those, a class of algorithms builds on the well-established variational framework, training a neural network as a regularization functional. Those approaches come with the advantage of a theoretical understanding and a stability theory that is built on existing results for variational regularization. We discuss various approaches for learning a regularization functional, aiming at giving an overview at the multiple directions investigated by the research community.

Suggested Citation

  • Sebastian Lunz, 2023. "Learned Regularizers for Inverse Problems," 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 31, pages 1133-1153, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98661-2_68
    DOI: 10.1007/978-3-030-98661-2_68
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