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| Image Decomposition Model OSV with L0 Sparse Regularization |
| Guodong Wang, Jie Xu, Zhenkuan Pan, Weizhong Zhang, Zhaojing Diao |
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Abstract Images decomposition attracts much attention in recent years. Many
variational methods were proposed. The most famous variational
decomposition model is OSV model because of its concise expression.
However, the OSV model suffers from many drawbacks. One is that the
discretization of the Eular- Lagrange equation is hard because the
4th order term was introduced. The other drawback is that the effect
of decomposition is weak. In this paper, we proposed a new
decomposition model with L0 sparse regularization. The L0 sparse
term is more sparse than traditional L1 norm. It is very useful in
many image processing applications. The combination of OSV model and
L0 regularization can improve the effect of traditional
decomposition. To improve the efficiency of the minimization, the
Split Bregman method for the improved OSV model is also designed.
Experimental results validate the proposed model.
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| DOI:10.12733/jics20105230 |
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