Learned Regularizers for Inverse Problems

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

Chapter 31 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 1133-1153 from Springer

Abstract: 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.

Keywords: Inverse problems; Variational regularization; Deep learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-98661-2_68

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DOI: 10.1007/978-3-030-98661-2_68

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