Denoising diffusion-weighted images using grouped iterative hard thresholding of multi-channel framelets

Jian Zhang, Geng Chen, Yong Zhang, Bin Dong, Dinggang Shen, Pew Thian Yap

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Noise in diffusion-weighted (DW) images increases the complexity of quantitative analysis and decreases the reliability of inferences. Hence, to improve analysis, it is often desirable to remove noise and at the same time preserve relevant image features. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of DW images. Our approach (1) employs the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders; (2) introduces a very efficient method for solving an 0 denoising problem that involves only thresholding and solving a trivial inverse problem; and (3) groups DW images acquired with neighboring gradient directions for collaborative denoising. Experiments using synthetic data with noncentral chi noise and real data with repeated scans confirm that our method yields superior performance compared with denoising using state-of-the-art methods such as non-local means.

Original languageEnglish
Title of host publicationComputational Diffusion MRI - MICCAI Workshop
EditorsAndrea Fuster, Yogesh Rathi, Marco Reisert, Enrico Kaden, Aurobrata Ghosh
PublisherSpringer Heidelberg
Number of pages11
ISBN (Print)9783319541297
Publication statusPublished - 2017
Externally publishedYes
EventMICCAI Workshop on Computational Diffusion MRI, CDMRI 2016 - Athens, Greece
Duration: 2016 Oct 172016 Oct 21

Publication series

NameMathematics and Visualization
ISSN (Print)1612-3786
ISSN (Electronic)2197-666X


OtherMICCAI Workshop on Computational Diffusion MRI, CDMRI 2016

ASJC Scopus subject areas

  • Modelling and Simulation
  • Geometry and Topology
  • Computer Graphics and Computer-Aided Design
  • Applied Mathematics


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