<abstract xmlns="http://www.w3.org/1999/xhtml"><p>With the aim to better preserve sharp edges and important structure features in the recovered image, this article researches an improved adaptive total variation regularization and H<sup>−1</sup> norm ﬁdelity based strategy for image decomposition and restoration. Computationally, for minimizing the proposed energy functional, we investigate an efﬁcient numerical algorithm—the split Bregman method, and brieﬂy prove its convergence. In addition, comparisons are also made with the classical OSV (Osher–Sole–Vese) model (Osher <italic>et al.</italic>, 2003) and the TV-Gabor model (Aujol <italic>et al.,</italic> 2006), in terms of the edge-preserving capability and the recovered results. Numerical experiments markedly demonstrate that our novel scheme yields signiﬁcantly better outcomes in image decomposition and denoising than the existing models.</p></abstract>

With the aim to better preserve sharp edges and important structure features in the recovered image, this article researches an improved adaptive total variation regularization and H−1 norm ﬁdelity based strategy for image decomposition and restoration. Computationally, for minimizing the proposed energy functional, we investigate an efﬁcient numerical algorithm—the split Bregman method, and brieﬂy prove its convergence. In addition, comparisons are also made with the classical OSV (Osher–Sole–Vese) model (Osher et al., 2003) and the TV-Gabor model (Aujol et al., 2006), in terms of the edge-preserving capability and the recovered results. Numerical experiments markedly demonstrate that our novel scheme yields signiﬁcantly better outcomes in image decomposition and denoising than the existing models.

An efficient algorithm for adaptive total variation based image decomposition and restoration

School of Mathematics and Computational Science Hunan University of Science and Technology, Xiangtan, Hunan 411201, China

College of Mathematics and Econometrics Hunan University, Changsha, Hunan 410082, China

Department of Information Technology Hunan Women’s University, Changsha, Hunan 410004, China

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

With the aim to better preserve sharp edges and important structure features in the recovered image, this article researches an improved adaptive total...

An efficient algorithm for adaptive total variation based image decomposition and restoration

Online veröffentlicht: 26. Juni 2014

Seitenbereich: 405 - 415

Eingereicht: 18. Apr. 2013

Akzeptiert: 28. Jan. 2014

DOI: https://doi.org/10.2478/amcs-2014-0031

Schlüsselwörter
image decomposition, image restoration, adaptive total variation, H norm, split Bregman method

© 2014 Xinwu Liu and Lihong Huang et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An efficient algorithm for adaptive total variation based image decomposition and restoration

Online veröffentlicht: 26. Juni 2014

Seitenbereich: 405 - 415

Eingereicht: 18. Apr. 2013

Akzeptiert: 28. Jan. 2014

DOI: https://doi.org/10.2478/amcs-2014-0031

Schlüsselwörterimage decomposition, image restoration, adaptive total variation, H norm, split Bregman method

© 2014 Xinwu Liu and Lihong Huang et al.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
image decomposition, image restoration, adaptive total variation, H norm, split Bregman method