Abstract
The block matching 3D (BM3D) is an efficient image model, which has found few applications other than its niche area of denoising. We will develop a magnetic resonance imaging (MRI) reconstruction algorithm, which uses decoupled iterations alternating over a denoising step realized by the BM3D algorithm and a reconstruction step through an optimization formulation. The decoupling of the two steps allows the adoption of a strategy with a varying regularization parameter, which contributes to the reconstruction performance. This new iterative algorithm efficiently harnesses the power of the nonlocal, image-dependent BM3D model. The MRI reconstruction performance of the proposed algorithm is superior to state-of-the-art algorithms from the literature. A convergence analysis of the algorithm is also presented.
Original language | English |
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Pages (from-to) | 430-440 |
Number of pages | 11 |
Journal | Journal of Mathematical Imaging and Vision |
Volume | 56 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Nov 2016 |
Bibliographical note
Publisher Copyright:© 2016, Springer Science+Business Media New York.
Keywords
- Block matching
- BM3D
- Compressed sensing
- Image reconstruction
- Magnetic resonance
- Sparsity