MR Image Reconstruction from under-sampled measurements using local and global sparse representations
MingJian Hong 1 , MengRan Lin 1 , Feng Liu 2 , and YongXin Ge 1
ChongQing University, ChongQing, ChongQing,
The University of Queensland, QLD, Australia
This work presented a new model by enforcing both local
and global sparsity, which captures both the patch-level
and global sparse structures of the anatomical images.
Using a model split approach, the image reconstruction
quality can be iteratively further improved. Our
simulation results demonstrate that, the proposed method
outperform those existing methods using only the
patch-level or global sparse structure.
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