文献类型：期刊文献

英文题名：Color image denoising based on guided filter and adaptive wavelet threshold

作者：Xin Sun[1]; Ning He[1]; Yu-Qing Zhang[2]; Xue-Yan Zhen[2]; Ke Lu[3]; Xiu-Ling Zhou[4]

第一作者：Xin Sun

机构：[1] Smart City Coll., Beijing Union Univ., Beijing, China; [2] Beijing Key Lab. of Inf. Service Eng., Beijing Union Univ., Beijing, China; [3] Univ. of Chinese Acad. of Sci., Beijing, China; [4] Beijing City Univ., Beijing, China

第一机构：北京联合大学继续教育学院

年份：0

卷号：2017

起止页码：5835020 (11 pp.)-5835020 (11 pp.)

外文期刊名：Applied Computational Intelligence and Soft Computing

收录：EI(收录号：17583186)

语种：英文

外文关键词：filtering theory - image colour analysis - image denoising - image texture - signal denoising - wavelet transforms

摘要：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.