  Approximate message passing (AMP) is an efficient iterative signal recovery
algorithm for compressed sensing (CS). For sensing matrices with independent
and identically distributed (i.i.d.) Gaussian entries, the behavior of AMP can
be asymptotically described by a scaler recursion called state evolution.
Orthogonal AMP (OAMP) is a variant of AMP that imposes a divergence-free
constraint on the denoiser. In this paper, we extend OAMP to incorporate
generic denoisers, hence the name D-OAMP. Our numerical results show that state
evolution predicts the performance of D-OAMP well for generic denoisers when
i.i.d. Gaussian or partial orthogonal sensing matrices are involved. We compare
the performances of denosing-AMP (D-AMP) and D-OAMP for recovering natural
images from CS measurements. Simulation results show that D-OAMP outperforms
D-AMP in both convergence speed and recovery accuracy for partial orthogonal
sensing matrices.
