Learned Iterative Reconstruction

Jonas Adler ()
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Jonas Adler: KTH – Royal Institute of Technology, Department of Mathematics

Chapter 19 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 751-771 from Springer

Abstract: 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.

Keywords: Inverse Problems; Deep Learning; Iterative reconstruction; Architectures (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-030-98661-2_67

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