Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts
Yanqiu Zeng, Baocan Zhang, Wei Zhao, Shixiao Xiao, Guokai Zhang, Haiping Ren, Wenbing Zhao, Yonghong Peng, Yutian Xiao, Yiwen Lu, Yongshuo Zong, Yimin Ding
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. This 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. The 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.