  Motivated by image recovery in magnetic resonance imaging (MRI), we propose a
new approach to solving linear inverse problems based on iteratively calling a
deep neural-network, sometimes referred to as plug-and-play recovery. Our
approach is based on the vector approximate message passing (VAMP) algorithm,
which is known for mean-squared error (MSE)-optimal recovery under certain
conditions. The forward operator in MRI, however, does not satisfy these
conditions, and thus we design new damping and initialization schemes to help
VAMP. The resulting DD-VAMP++ algorithm is shown to outperform existing
algorithms in convergence speed and accuracy when recovering images from the
fastMRI database for the practical case of Cartesian sampling.
