Abstract and Applied Analysis

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

Dali Chen, YangQuan Chen, and Dingyu Xue

Full-text: Open access

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 O 1 / N 2 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 O 1 / N 2 convergence rate.

Article information

Source
Abstr. Appl. Anal., Volume 2013, Special Issue (2013), Article ID 585310, 10 pages.

Dates
First available in Project Euclid: 26 February 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1393450248

Digital Object Identifier
doi:10.1155/2013/585310

Zentralblatt MATH identifier
1364.94091

Citation

Chen, Dali; Chen, YangQuan; Xue, Dingyu. Fractional-Order Total Variation Image Restoration Based on Primal-Dual Algorithm. Abstr. Appl. Anal. 2013, Special Issue (2013), Article ID 585310, 10 pages. doi:10.1155/2013/585310. https://projecteuclid.org/euclid.aaa/1393450248


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