Data-Informed Regularization for Inverse and Imaging Problems

Jonathan Wittmer () and Tan Bui-Thanh ()
Additional contact information
Jonathan Wittmer: UT Austin, Department of Aerospace Engineering and Engineering Mechanics
Tan Bui-Thanh: The Oden Institute for Computational Engineering and Sciences, UT Austin, Department of Aerospace Engineering and Engineering Mechanics

Chapter 35 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 1235-1272 from Springer

Abstract: Abstract This chapter presents a new regularization method for inverse and imaging problems, called data-informed (DI) regularization, that implicitly avoids regularizing the data-informed directions. Our approach is inspired by and has a rigorous root in disintegration theory. We shall, however, present an elementary and constructive path using the classical truncated SVD and Tikhonov regularization methods. Deterministic and statistical properties of the DI approach are rigorously discussed, and numerical results for image deblurring, image denoising, and X-ray tomography are presented to verify our findings.

Keywords: Inverse problems; Imaging; Tikhonov regularization; Truncated SVD; Data-informed regularization (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-98661-2_77

Ordering information: This item can be ordered from
http://www.springer.com/9783030986612

DOI: 10.1007/978-3-030-98661-2_77

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().