Fractional-Order Total Variation Image Restoration Based on Primal-Dual Algorithm

Dali Chen, YangQuan Chen and Dingyu Xue

Abstract and Applied Analysis, 2013, vol. 2013, 1-10

Abstract:

This paper proposes a fractional-order total variation image denoising algorithm based on the primal-dual method, which provides a much more elegant and effective way of treating problems of the algorithm implementation, ill-posed inverse, convergence rate, and blocky effect. The fractional-order total variation model is introduced by generalizing the first-order model, and the corresponding saddle-point and dual formulation are constructed in theory. In order to guarantee convergence rate, the primal-dual algorithm was used to solve the constructed saddle-point problem, and the final numerical procedure is given for image denoising. Finally, the experimental results demonstrate that the proposed methodology avoids the blocky effect, achieves state-of-the-art performance, and guarantees convergence rate.

Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlaaa:585310

DOI: 10.1155/2013/585310

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