Author/Authors :
Zeng, Yanqiu Chengyi University College - Jimei University - Xiamen, China , Zhang, Baocan Chengyi University College - Jimei University - Xiamen, China , Zhao, Wei Chengyi University College - Jimei University - Xiamen, China , Xiao, Shixiao Chengyi University College - Jimei University - Xiamen, China , Zhang, Guokai School of Software Engineering - Tongji University - Shanghai, China , Ren, Haiping Jiangxi University of Science and Technology - Nanchang, China , Zhao, Wenbing Department of Electrical Engineering and Computer Science - Cleveland State University - Cleveland, USA , Peng, Yonghong Faculty of Computer Science - University of Sunderland - Sunderland, UK , Xiao, Yutian School of Informatics - Xiamen University - Xiamen, China , Lu, Yiwen Department of Computer Science - Tongji University - Shanghai, China , Zong, Yongshuo Department of Computer Science - Tongji University - Shanghai, China , Ding, Yimin Tongji University - Shanghai, China
Abstract :
Magnetic resonance (MR) images are often contaminated by Gaussian noise, an electronic noise caused by the random thermal
motion of electronic components, which reduces the quality and reliability of the images. /is paper puts forward a hybrid
denoising algorithm for MR images based on two sparsely represented morphological components and one residual part. To begin
with, decompose a noisy MR image into the cartoon, texture, and residual parts by MCA, and then each part is denoised by using
Wiener filter, wavelet hard threshold, and wavelet soft threshold, respectively. Finally, stack up all the denoised subimages to
obtain the denoised MR image. /e experimental results show that the proposed method has significantly better performance in
terms of mean square error and peak signal-to-noise ratio than each method alone.
Keywords :
Algorithm , Cartoon , Texture , MCA , MR
