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Image restoration: Total variation, wavelet frames, and beyond


Authors: Jian-Feng Cai, Bin Dong, Stanley Osher and Zuowei Shen
Journal: J. Amer. Math. Soc. 25 (2012), 1033-1089
MSC (2010): Primary 35A15, 41A25, 42C40, 45Q05, 65K10, 68U10
DOI: https://doi.org/10.1090/S0894-0347-2012-00740-1
Published electronically: May 17, 2012
MathSciNet review: 2947945
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Abstract: The variational techniques (e.g. the total variation based method) are well established and effective for image restoration, as well as many other applications, while the wavelet frame based approach is relatively new and came from a different school. This paper is designed to establish a connection between these two major approaches for image restoration. The main result of this paper shows that when spline wavelet frames of are used, a special model of a wavelet frame method, called the analysis based approach, can be viewed as a discrete approximation at a given resolution to variational methods. A convergence analysis as image resolution increases is given in terms of objective functionals and their approximate minimizers. This analysis goes beyond the establishment of the connections between these two approaches, since it leads to new understandings for both approaches. First, it provides geometric interpretations to the wavelet frame based approach as well as its solutions. On the other hand, for any given variational model, wavelet frame based approaches provide various and flexible discretizations which immediately lead to fast numerical algorithms for both wavelet frame based approaches and the corresponding variational model. Furthermore, the built-in multiresolution structure of wavelet frames can be utilized to adaptively choose proper differential operators in different regions of a given image according to the order of the singularity of the underlying solutions. This is important when multiple orders of differential operators are used in various models that generalize the total variation based method. These observations will enable us to design new methods according to the problems at hand, hence, lead to wider applications of both the variational and wavelet frame based approaches. Links of wavelet frame based approaches to some more general variational methods developed recently will also be discussed.


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Additional Information

Jian-Feng Cai
Affiliation: Department of Mathematics, The University of Iowa, Iowa City, Iowa 52242-1419
Email: jianfeng-cai@uiowa.edu

Bin Dong
Affiliation: Department of Mathematics, The University of Arizona, 617 North Santa Rita Avenue, Tucson, Arizona 85721-0089
Email: dongbin@math.arizona.edu

Stanley Osher
Affiliation: Department of Mathematics, University of California, Los Angeles, 405 Hilgard Avenue, Los Angeles, California 90095-1555
Email: sjo@math.ucla.edu

Zuowei Shen
Affiliation: Department of Mathematics, National University of Singapore, Block S17, 10 Lower Kent Ridge Road, Singapore 119076
Email: matzuows@nus.edu.sg

DOI: https://doi.org/10.1090/S0894-0347-2012-00740-1
Keywords: Image restoration, total variation, variational method, (tight) wavelet frames, framelets, split Bregman, $Γ$-convergence, pointwise convergence.
Received by editor(s): April 3, 2011
Received by editor(s) in revised form: March 26, 2012
Published electronically: May 17, 2012
Article copyright: © Copyright 2012 American Mathematical Society
The copyright for this article reverts to public domain 28 years after publication.

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