  In this paper, the problem of Magnetic Resonance (MR) image reconstruction
from partial Fourier samples has been considered. To this aim, we leverage the
evidence that MR images are sparser than their zero-filled reconstructed ones
from incomplete Fourier samples. This information can be used to define an
optimization problem which searches for the sparsest possible image conforming
with the available Fourier samples. We solve the resulting problem using the
well-known Alternating Direction Method of Multipliers (ADMM). Unlike most
existing methods that work with small over-lapping image patches, the proposed
algorithm considers the whole image without dividing it into small blocks.
Experimental results prominently confirm its promising performance and
advantages over the existing methods.
