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

Xinwu Liu; Lihong Huang

International Journal of Applied Mathematics and Computer Science (2014)

  • Volume: 24, Issue: 2, page 405-415
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Xinwu Liu, and Lihong Huang. "An efficient algorithm for adaptive total variation based image decomposition and restoration." International Journal of Applied Mathematics and Computer Science 24.2 (2014): 405-415. <http://eudml.org/doc/271926>.

@article{XinwuLiu2014,
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.},
author = {Xinwu Liu, Lihong Huang},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {image decomposition; image restoration; adaptive total variation; $H^\{-1\}$ norm; split Bregman method; norm},
language = {eng},
number = {2},
pages = {405-415},
title = {An efficient algorithm for adaptive total variation based image decomposition and restoration},
url = {http://eudml.org/doc/271926},
volume = {24},
year = {2014},
}

TY - JOUR
AU - Xinwu Liu
AU - Lihong Huang
TI - An efficient algorithm for adaptive total variation based image decomposition and restoration
JO - International Journal of Applied Mathematics and Computer Science
PY - 2014
VL - 24
IS - 2
SP - 405
EP - 415
AB - 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.
LA - eng
KW - image decomposition; image restoration; adaptive total variation; $H^{-1}$ norm; split Bregman method; norm
UR - http://eudml.org/doc/271926
ER -

References

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