Color Image Denoising Based on Guided Filter and Adaptive Wavelet Threshold. (6th November 2017)

• Record Type: Journal Article
• Authors: Sun, Xin; He, Ning; Zhang, Yu-Qing; Zhen, Xue-Yan; Lu, Ke; Zhou, Xiu-Ling
• Other Names: Ejbali Ridha Academic Editor.
• Is Part Of: Applied computational intelligence and soft computing. Volume 2017(2017)
• Publication Date: 2017-11-06
• Subjects: Computational intelligence -- Periodicals; Soft computing -- Periodicals; 006.305
• DOI: 10.1155/2017/5835020
• Languages: English
• ISSNs: 1687-9724

Abstract

Abstract : 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.