Improved adaptive wavelet threshold for image denoising

Authors:
Wei Zhang;Fei Yu;Hong-Mi Guo
Affiliations:
School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China and School of Control Science and Engineering, Shandong University, Jinan, China;School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China;School of Automation and Electronic Engineering, Qingdao University of Science and Technology, Qingdao, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

Adaptive wavelet threshold for Bayes shrink (Bayes threshold) is a simple and effective method for image denoising. Multiple wavelet representations have excellent performance in image denoising. In this paper, combining the multiple wavelet representations with the Bayes threshold and using their advantages in image denoising, proposes a new image denoising algorithm which called M-Bayes threshold. It is simple and effective. Simulation results show that the proposed M-Bayes threshold can achieve the state-of-the-art image denoising performance at the low computational complexity.