Image Decomposition Based on Curvelet and Wave Atom

Authors:
Chengwu Lu
Affiliations:
School of Mathematics and Statistics, Chongqing University of, Arts and Sciences, Chongqing, China 402160 and School of Science, Xidian University, Xi'an, China 710071
Venue:
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Year:
2008

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Abstract

To separate oscillating parts such as texture and noise from piecewise smooth parts, a new variational image decomposition model is presented, which well improve the novel Starck's model. The second generation curvelets and wave atoms are used to represent structure and texture respectively. The total variation semi-norm is added for restricting structure parts. The generalized homogeneous Besov norm proposed by Meyer is used to constrain noisy components. Finally, the Basis Pursuit Denoisiing algorithm is used to solve the new model. Experiments show that the approach is very robust to noise, and that can keep edges and textures stably.