Background:
In order to overcome the limitation of long scanning time, compressive
sensing (CS) technology exploits the sparsity of image in some transform domain to reduce the
amount of acquired data. Therefore, CS has been widely used in magnetic resonance imaging
(MRI) reconstruction.
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Discussion: Blind compressed sensing enables to recover the image successfully from highly under-
sampled measurements, because of the data-driven adaption of the unknown transform basis
priori. Moreover, analysis-based blind compressed sensing often leads to more efficient signal reconstruction
with less time than synthesis-based blind compressed sensing. Recently, some experiments
have shown that nonlocal low-rank property has the ability to preserve the details of the image for
MRI reconstruction.
Methods:
Here, we focus on analysis-based blind compressed sensing, and combine it with additional
nonlocal low-rank constraint to achieve better MR images from fewer measurements. Instead
of nuclear norm, we exploit non-convex Schatten p-functionals for the rank approximation.
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Results & Conclusion: Simulation results indicate that the proposed approach performs better than
the previous state-of-the-art algorithms.
Robust Subspace Discovery through Supervised Low-Rank Constraints