Sparse Representation Based MRI Denoising with Total Variation

摘要：

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.

作者: Lijun Bao Wanyu Liu Yuemin Zhu Zhaobang Pu Isabelle E.Magnin

作者单位: HIT–INSA Sino French Research Center for Biomedical Imaging,Harbin Institute of Technology,Harbin,15 HIT–INSA Sino French Research Center for Biomedical Imaging,Harbin Institute of Technology,Harbin,15 CREATIS-LRMN; CNRS UMR 5220; INSA of Lyon; Villeurbanne,69100,France;HIT–INSA Sino French Research C

会议类型: 国际会议

会议名称: 9th International Conference on Signal Processing(第九届国际信号处理学术会议)(ICSP08)

会议地点: 北京

会议语种:英文

在线出版日期: 2008-10-26（万方平台首次上网日期，不代表论文的发表时间）