Low-Dose CT Reconstruction with Non-Local Functionals
Article No.: 61, Pages 1 - 5
Abstract
In medical CT, the X-ray exposure dose reduction is expected. As a decrease in the dose, the image is degraded due to the noise. Therefore, the development of the noise reduction algorithm while maintaining image quality is an important issue. To suppress the noise, the penalized least squares method is effective. Recently, non-local total variation (NLTV) and non-local structure tensor TV (NLSTV) have been reported. These functional penalties have shown excellent denoising performance of the natural image. In this paper, we apply the functionals to the low-dose CT reconstruction problem. The reconstruction method and the comparison between TV, NLTV, and NLSTV are shown.
References
[1]
A. Buades, B. Coll, and J. M. Morel. 2005. A Non-Local Algorithm for Image Denoising. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), Vol. 2. IEEE, 60--65.
[2]
A. Chambolle. 2004. An Algorithm for Total Variation Minimization and Applications. Journal of Mathematical Imaging and Vision 20, 1-2 (2004), 89--97.
[3]
A. Chambolle and T. Pock. 2011. A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging. Journal of Mathematical Imaging and Vision 40, 1 (2011), 120--145.
[4]
G. Gilboa and S. Osher. 2008. Nonlocal Operators with Applications to Image Processing. Multiscale Modeling & Simulation 7, 3 (2008), 1005--1028.
[5]
J. Bigun and G. H. Granlund. 1987. Optimal Orientation Detection of Linear Symmetry. In Proceedings of the IEEE First International Conference on Computer Vision.
[6]
S. Lefkimmiatis, A. Roussos, M. Unser, and P. Maragos. 2013. Convex Generalizations of Total Variation Based on the Structure Tensor with Applications to Inverse Problems. In Lecture Notes in Computer Science. Vol. 7893 LNCS. 48--60.
[7]
S. Lefkimmiatis, A. Roussos, P. Maragos, and M. Unser. 2015. Structure Tensor Total Variation. SIAM Journal on Imaging Sciences 8, 2 (2015), 1090--1122.
[8]
S. Lefkimmiatis and S. Osher. 2015. Nonlocal Structure Tensor Functionals for Image Regularization. IEEE Transactions on Computational Imaging 1, 1 (2015), 16--29.
[9]
S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, T. Yu, and T. scikit-image contributors. 2014. scikit-image: image processing in {P}ython. PeerJ 2 (2014), e453.
[10]
W. Forestner and E. Gulch. 1987. A fast operator for detection and precise location of distinct points, corners and centres of circular features. In ISPRS Intercommission Workshop.
Index Terms
- Low-Dose CT Reconstruction with Non-Local Functionals
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Published In
August 2019
584 pages
ISBN:9781450376259
DOI:10.1145/3387168
Copyright © 2019 ACM.
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Association for Computing Machinery
New York, NY, United States
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Published: 25 May 2020
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ICVISP 2019
ICVISP 2019: 3rd International Conference on Vision, Image and Signal Processing
August 26 - 28, 2019
BC, Vancouver, Canada
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ICVISP 2019 Paper Acceptance Rate 126 of 277 submissions, 45%;
Overall Acceptance Rate 186 of 424 submissions, 44%
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