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Applied Computational Intelligence and Soft Computing
Volume 2017, Article ID 5835020, 11 pages
Research Article

Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold

1Smart City College, Beijing Union University, Beijing 100101, China
2Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
3University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China
4Beijing City University, Beijing 100083, China

Correspondence should be addressed to Ning He; nc.ude.uub@gninehtxx

Received 29 May 2017; Revised 20 August 2017; Accepted 21 August 2017; Published 6 November 2017

Academic Editor: Ridha Ejbali

Copyright © 2017 Xin Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the process of denoising color images, it is very important to enhance the edge and texture information of the images. Image quality can usually be improved by eliminating noise and enhancing contrast. Based on the adaptive wavelet threshold shrinkage algorithm and considering structural characteristics on the basis of color image denoising, this paper describes a method that further enhances the edge and texture details of the image using guided filtering. The use of guided filtering allows edge details that cannot be discriminated in grayscale images to be preserved. The noisy image is decomposed into low-frequency and high-frequency subbands using discrete wavelets, and the contraction function of threshold shrinkage is selected according to the energy in the vicinity of the wavelet coefficients. Finally, the edge and texture information of the denoised color image are enhanced by guided filtering. When the guiding image is the original noiseless image itself, the guided filter can be used as a smoothing operator for preserving edges, resulting in a better effect than bilateral filtering. The proposed method is compared with the adaptive wavelet threshold shrinkage denoising algorithm and the bilateral filtering algorithm. Experimental results show that the proposed method achieves superior color image denoising compared to these conventional techniques.