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| Total Variation-Curvelet Joint Sparse Representation Model and Primal-Dual Algorithm |
| YU Yi-Bin, LI Qi-Da, GAN Jun-Ying, SUN Jian-Jun |
| School of Information Engineering, Wuyi University, Jiangmen 529020 |
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Abstract Total variation model is widely used in machine vision due to its strong ability of capturing the details of the images and the videos. Curvelet transform can capture the edges and curved lines of the 2D signals easily. Combining both advantages, a class of joint sparse representation model is proposed, i.e. total variation and curvelet (TVC). This model can represent the characteristics of the 2D signals more effectively. Primal-dual (PD) scheme is used to solve the model, which is called PDTVC algorithm. Experimental results show that PDTVC outperforms the existing algorithms in both subjective visual effect and objective image qualities. PDTVC can be applied to various challenging image processing tasks as well, such as deblurring and super resolution.
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Received: 05 February 2013
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2013, Vol. 26 
