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“Decoupled
Algorithm for MRI Reconstruction Using Nonlocal Block Matching Model: BM3D-MRI”
E.M. Eksioglu,
“Decoupled Algorithm for MRI Reconstruction Using Nonlocal Block Matching
Model: BM3D-MRI”, Journal of Mathematical Imaging and Vision, vol. 56,
no. 3, pp. 430-440, November 2016. [pdf]
MATLAB
code to realize the BM3D-MRI algorithm:
The above given BM3D-MRI toolbox should be
used in conjunction with the BM3D denoising toolbox available online from the BM3D
main page (http://www.cs.tut.fi/~foi/GCF-BM3D/).
Download the (legacy) BM3D denoising
toolbox (BM3D.zip)
and put the unzipped folder “BM3D” under the main folder of the BM3D-MRI
toolbox.
Also download the modified BM3D denoising
function here, and put it under the main
folder of the BM3D-MRI toolbox.
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.
Keywords:
Image
reconstruction, Magnetic resonance, Block matching, BM3D, Compressed sensing, Sparsity
