Volume 9 Number 8 (Aug. 2014)
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JSW 2014 Vol.9(8): 2105-2111 ISSN: 1796-217X
doi: 10.4304/jsw.9.8.2105-2111

Variational Image Decomposition Model OSV with General Diffusion Regularization

Guodong Wang1, Jie Xu2, Zhenkuan Pan1, Jinbao Yang1
1College of Information Engineering, Qingdao University, Qingdao, China
2College of Physics Science, Qingdao University, Qingdao, China

Abstract—Image decomposition technology is a very useful tool for image analysis. Images contain structural component and textural component which can be decomposed by variational methods such as VO (Vese- Osher) and OSV (Osher-Sole-Vese) models. OSV model is a powerful tool for image decomposition but the minimization is a hard problem because of solving the 4th order partial differential equations with complex finite difference scheme for Laplacian of curvature. In this paper we proposed an improved OSV model with general diffusion regularization. The general diffusion terms can be TV(Total Variation), Nonlinear diffusion(Perona and Malik) and Charbonnier regulerizers. Additionally, we also use L1 norm as data term inspired by TV-L1 method. We also use Split Bregman method for the easy implementation of the improved OSV model. Experiments show the proposed method is a valid method for image decomposition.

Index Terms—image decomposition, OSV model, Split Bregman method, TV-L1, general diffusion regularization


Cite: Guodong Wang, Jie Xu, Zhenkuan Pan, Jinbao Yang, "Variational Image Decomposition Model OSV with General Diffusion Regularization," Journal of Software vol. 9, no. 8, pp. 2105-2111, 2014.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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