The Image Deblurring Problem: Matrices, Wavelets, and Multilevel Methods

The image deblurring problem consists of reconstructing images from blur and noise contaminated available data. In this AMS Notices article, we provide an overview of some well known numerical linear algebra techniques that are use for solving this problem. In particular, we start by carefully descr...

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Published in:arXiv.org 2022-01
Main Authors: Austin, David, Español, Malena I, Pasha, Mirjeta
Format: Article
Language:English
Subjects:
Blurring
Image reconstruction
Linear algebra
Mathematical analysis
Operators (mathematics)
Regularization
Regularization methods
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Español, Malena I
Pasha, Mirjeta
description The image deblurring problem consists of reconstructing images from blur and noise contaminated available data. In this AMS Notices article, we provide an overview of some well known numerical linear algebra techniques that are use for solving this problem. In particular, we start by carefully describing how to represent images, the process of blurring an image and modeling different kind of added noise. Then, we present regularization methods such as Tikhonov (on the standard and general form), Total Variation and other variations with sparse and edge preserving properties. Additionally, we briefly overview some of the main matrix structures for the blurring operator and finalize presenting multilevel methods that preserve such structures. Numerical examples are used to illustrate the techniques described.
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subjects Blurring
Image reconstruction
Linear algebra
Mathematical analysis
Operators (mathematics)
Regularization
Regularization methods
title The Image Deblurring Problem: Matrices, Wavelets, and Multilevel Methods
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