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

• Autorzy: Xinwu Liu , Lihong Huang
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

image decomposition; image restoration; adaptive total variation; $H^{-1}$ norm; split Bregman method

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: 24
• Numer: 2
• Strony: 405-415

Daty

wydano

2014

otrzymano

2013-04-18

poprawiono

2014-01-06

poprawiono

2014-01-28

Twórcy

autor

Xinwu Liu

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

autor

Lihong Huang

• College of Mathematics and Econometrics, Hunan University, Changsha, Hunan 410082, China
• Department of Information Technology, Hunan Women's University, Changsha, Hunan 410004, China

Bibliografia

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Identyfikatory

DOI

10.2478/amcs-2014-0031