A Smoothing Fast Iterative Shrinkage/Thresholding Algorithm for Compressed Mr Imaging

In this paper, we propose a smoothing fast iterative shrinkage/thresholding algorithm for compressed MR image reconstruction. The basic idea of our algorithm is to apply a smoothing technique to the minimization problem of a linear combination of three terms corresponding to a least squares data fitting, total variation and $l_{1}$ norm regularization, which has been shown to be very powerful for the MR image reconstruction, and then minimize this approximation using the well-known fast iterative shrinkage/thresholding algorithm. Experimental results show that the quality of restored MR images by our proposed method is competitive with those restored by the previous methods for compressed MR image reconstruction.