  The denoising-based approximate message passing (D-AMP) methodology, recently
proposed by Metzler, Maleki, and Baraniuk, allows one to plug in sophisticated
denoisers like BM3D into the AMP algorithm to achieve state-of-the-art
compressive image recovery. But AMP diverges with small deviations from the
i.i.d.-Gaussian assumption on the measurement matrix. Recently, the vector AMP
(VAMP) algorithm has been proposed to fix this problem. In this work, we show
that the benefits of VAMP extend to D-VAMP.
