  Approximate message passing (AMP) methods have gained recent traction in
sparse signal recovery. Additional information about the signal, or \emph{side
information} (SI), is commonly available and can aid in efficient signal
recovery. This work presents an AMP-based framework that exploits SI and can be
readily implemented in various settings for which the SI results in separable
distributions. To illustrate the simplicity and applicability of our approach,
this framework is applied to a Bernoulli-Gaussian (BG) model and a time-varying
birth-death-drift (BDD) signal model, motivated by applications in channel
estimation. We develop a suite of algorithms, called AMP-SI, and derive
denoisers for the BDD and BG models. Numerical evidence demonstrating the
advantages of our approach are presented alongside empirical evidence of the
accuracy of a proposed state evolution.
