Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts

Zeng, Yanqiu, Zhang, Baocan, Zhao, Wei, Xiao, Shixiao, Zhang, Guokai, Ren, Haiping, Zhao, Wenbing, Peng, Yonghong, Xiao, Yutian, Lu, Yiwen, Zong, Yongshuo and Ding, Yimin (2020) Magnetic Resonance Image Denoising Algorithm Based on Cartoon, Texture, and Residual Parts. Computational and Mathematical Methods in Medicine, 2020. ISSN 1748-6718

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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.

Item Type: Article
Additional Information: ** From Hindawi via Jisc Publications Router ** History: received 08-02-2020; accepted 06-03-2020; pub-print 01-04-2020; epub 01-04-2020. ** Licence for this article:
Uncontrolled Keywords: Research Article
Divisions: Faculty of Technology > School of Computer Science
Related URLs:
SWORD Depositor: Publication Router
Depositing User: Publication Router
Date Deposited: 15 Apr 2020 17:35
Last Modified: 30 Sep 2020 11:03
ORCID for Yanqiu Zeng: ORCID iD
ORCID for Wei Zhao: ORCID iD
ORCID for Guokai Zhang: ORCID iD
ORCID for Haiping Ren: ORCID iD
ORCID for Yonghong Peng: ORCID iD

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