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Journal of Information Science and Engineering, Vol. 32 No. 3, pp. 747-762 (May 2016)


An Adaptive Fractional-Order Variation Method for Multiplicative Noise Removal*


DAN TIAN1,2, YINGKUI DU1,2 AND DALI CHEN2
1Department of Information Engineering
Shenyang University
Shenyang, 110044 P.R. China
2State key Laboratory of Robotics
Shenyang Institute of Automation
Chinese Academy of Sciences
Shenyang, 110016 P.R. China
E-mail: www.sltd2008@163.com

This paper aims to develop a convex fractional-order variation model for image multiplicative noise removal, where the regularization parameter can be adjusted adaptively according to balancing principle at each iterations to control the trade-off between the fitness and smoothness of the denoised images. In the light of the saddle-point theory, a primal-dual algorithm has been applied to solve the proposed model, and the convergence of the algorithm is guaranteed. Simulations with comparisons are carried out to demonstrate the details preserving ability and the fast property of our proposed denoising method.

Keywords: gamma noise, image denoising, fractional differential, primal-dual algorithm, adaptive regularization parameter

Full Text () Retrieve PDF document (201605_13.pdf)

Received November 11, 2014; revised August 16 & October 28, 2015; accepted November 8, 2015.
Communicated by Jen-Hui Chuang.
* This work was supported by the State Key Laboratory of Robotics, China (Grant No. 2015008), the China National Nature Science Foundation (Grant No. 61503274), and the Liaoning province Nature Science Foundation (Grant No. 2015020158-301).