ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.049
A Shearlet-TV Regularization Approach for Image Denoising in the ADMM Framework
Abstract— The Shearlet transform is widely used in images denoising. However, the Shearlet based
denoising method suffers from Gibbs artifacts because of the cut off in Fourier domain. In order to suppress
the Gibbs artifacts, we propose a multi-constrained denoising model by using Shearlet transform and total
variation. Considering that the proposed model is a multi-constrained problem, alternating direction method
of multipliers is used to decouple the complex issue into some sub problems, which are easier to solve.
Experiments are carried out to show the performance of proposed method. The experimental results indicate
that the proposed method effectively relive the Gibbs artifacts of Shearlet transform.
Index Terms— shearlet, TV, ADMM, image denoising
Xingguo Liu, Yingpin Chen, Zhenming Pen
School of Optoelectronic Information, University of Electronic Science and Technology of China, CHINA
Chongqing College of Electronic Engineering, CHINA
College of Communication Engineering, Chongqing University, CHINA
Cite: Xingguo Liu, Yingpin Chen, Zhenming Pen, Juan Wu, "A Shearlet-TV Regularization Approach for Image Denoising in the ADMM Framework," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 283-287, Beijing, 25-27 June, 2017.