Total variation-based speckle reduction using multi-grid algorithm for ultrasound images

Authors:
Chen Sheng;Yang Xin;Yao Liping;Sun Kun
Affiliations:
Institution of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, P.R. China;Institution of Image Processing and Pattern Recognition, Shanghai Jiaotong University, Shanghai, P.R. China;Shanghai Children's Medical Center, Shanghai Second Medical University, Shanghai, P.R. China;Shanghai Children's Medical Center, Shanghai Second Medical University, Shanghai, P.R. China
Venue:
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Year:
2005

Citing 2
Cited 2

Nonlinear total variation based noise removal algorithms

Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
Fast, robust total variation-based reconstruction of noisy, blurred images

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Nonlocal means-based speckle filtering for ultrasound images

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Total variation regularization algorithms for images corrupted with different noise models: a review

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Abstract

This paper presents an approach for speckle reduction and coherence enhancement of ultrasound images based on total variation (TV) minimization. The proposed method can preserve information associated with resolved object structures while reducing the speckle noise. However, since the equation system deduced by the TV-based method is a strongly nonlinear partial differential equation (PDE) system, the convergence rate is very slow when using standard numerical optimization techniques. So in this paper, we introduce the nonlinear multi-grid algorithm to solve this system. Numerical results indicate that the image can be recovered with satisfied result even contamination of strong noise using the proposed method and the algorithm of nonlinear multi-grid has more efficiency than the conventional numerical techniques such as conjugate gradient (CG).