NDLI: Sparse representation based MRI denoising with total variation

Content Provider	IEEE Xplore Digital Library
Author	Lijun Bao Wanyu Liu Yuemin Zhu Zhaobang Pu Magnin, I.E.
Copyright Year	2008
Description	Author affiliation: CREATIS-LRMN, INSA of Lyon, Villeurbanne (Yuemin Zhu; Magnin, I.E.) \|\| HIT-INSA Sino French Res. Center for Biomed. Imaging, Harbin Inst. of Technol., Harbin (Lijun Bao; Wanyu Liu; Zhaobang Pu)
Abstract	Diffusion tensor magnetic resonance imaging is a newly developed imaging technique; however, this technique is noise sensitive. This paper presents a novel method for sparse representation denoising of MR images that propose sparse representation of the corrupted images with the knowledge of the Rician noise model. The proposed model inferring the prior that MR images are composed of several separated regions with uniform intensity, therefore, total variation can be combined to further smooth every region. Since sparse representation performs well in extracting features from images, coupled with the total variation regularization, the method offers excellent combination of noise removal and edge preservation. The experiment results demonstrate that the proposed method preserves most of the fine structure in cardiac diffusion weighted images.
Starting Page	2154
Ending Page	2157
File Size	233914
Page Count	4
File Format	PDF
ISBN	9781424421787
DOI	10.1109/ICOSP.2008.4697573
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-26
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Magnetic resonance imaging Noise reduction Diffusion tensor imaging Magnetic noise Noise level Rician channels Signal to noise ratio Filters Biomedical imaging Image denoising
Content Type	Text
Resource Type	Article

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