A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing

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A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing

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Title: A Weighted Difference of Anisotropic and Isotropic Total Variation Model for Image Processing
Author(s):
Lou, Yifei (UT Dallas);
Zeng, T.;
Osher, S.;
Xin, J.
Item Type: Article
Keywords: Show Keywords
Abstract: We propose a weighted difference of anisotropic and isotropic total variation (TV) as a regularization for image processing tasks, based on the well-known TV model and natural image statistics. Due to the form of our model, it is natural to compute via a difference of convex algorithm (DCA). We draw its connection to the Bregman iteration for convex problems and prove that the iteration generated from our algorithm converges to a stationary point with the objective function values decreasing monotonically. A stopping strategy based on the stable oscillatory pattern of the iteration error from the ground truth is introduced. In numerical experiments on image denoising, image deblurring, and magnetic resonance imaging (MRI) reconstruction, our method improves on the classical TV model consistently and is on par with representative state-of-the-art methods.
Publisher: Society for Industrial and Applied Mathematics Publications
ISSN: 1936-4954
Persistent Link: http://hdl.handle.net/10735.1/4808
http://dx.doi.org/10.1137/14098435X
Bibliographic Citation: Lou, Y., T. Zeng, S. Osher, and J. Xin. 2015. "A weighted difference of anisotropic and isotropic total variation model for image processing." SIAM Journal on Imaging Sciences 8(3), doi: 10.1137/14098435X.
Terms of Use: ©2015 Society for Industrial and Applied Mathematics. "Unauthorized reproduction of this article is prohibited."

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