An Edge-preserving Variational Method for Image Decomposition

XU Chen; LI Min; SUN Xiaoli

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Article Navigation > Chinese Journal of Electronics > 2013 > 22(1): 109-113

XU Chen, LI Min, SUN Xiaoli, “An Edge-preserving Variational Method for Image Decomposition,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 109-113, 2013,

Citation:

XU Chen, LI Min, SUN Xiaoli, “An Edge-preserving Variational Method for Image Decomposition,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 109-113, 2013,

XU Chen, LI Min, SUN Xiaoli, “An Edge-preserving Variational Method for Image Decomposition,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 109-113, 2013,

Citation:

XU Chen, LI Min, SUN Xiaoli, “An Edge-preserving Variational Method for Image Decomposition,” Chinese Journal of Electronics, vol. 22, no. 1, pp. 109-113, 2013,

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An Edge-preserving Variational Method for Image Decomposition

College of Mathematics and Computational Science, Shenzhen University, Shenzhen 518060, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61070087), the National Natural Science Funds for Young Scholar (No.61001183) and Guangdong University-industry Cooperation Projects (No.2009B090300355).

Received Date: 2011-03-01
Rev Recd Date: 2011-06-01
Publish Date: 2013-01-05

Abstract

Abstract

Variational methods for image decomposition have gained considerable attention in recent years. In such approaches, an image usually can be decomposed into a geometrical (or structure) component and a textured (or noise) feature. In this paper we propose an edge-preserving variational model which can split an image into four components: a first one containing the structure of the image, a second one the texture of the image, a third one the noise and a forth one the edge. Our decomposition model relies on the use of three different terms: the edgepreserving regularization for the geometrical component and the edge, a negative Sobolev norm for the texture, and a negative Besov norm for the noise. We explicitly give numerical scheme that is the synthesis of a projection algorithm, a redundant wavelet (or curvelet) soft threshold and two coupled Partial differential equations (PDE’s). Finally we show image decomposition results on synthetic and real image.
- Image decomposition,
- Edge-preserving,
- Structure,
- Texture,
- Noise,
- Edge,
- Variational approach

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References(10)

References

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