  Plug and play (P&P) algorithms iteratively apply highly optimized image
denoisers to impose priors and solve computational image reconstruction
problems, to great effect. However, in general the "effective noise", that is
the difference between the true signal and the intermediate solution, within
the iterations of P&P algorithms is neither Gaussian nor white. This fact makes
existing denoising algorithms suboptimal.
  In this work, we propose a CNN architecture for removing colored Gaussian
noise and combine it with the recently proposed VDAMP algorithm, whose
effective noise follows a predictable colored Gaussian distribution. We apply
the resulting denoising-based VDAMP (D-VDAMP) algorithm to variable density
sampled compressive MRI where it substantially outperforms existing techniques.
