  We introduce a new algorithm for regularized reconstruction of multispectral
(MS) images from noisy linear measurements. Unlike traditional approaches, the
proposed algorithm regularizes the recovery problem by using a prior specified
\emph{only} through a learned denoising function. More specifically, we propose
a new accelerated gradient method (AGM) variant of regularization by denoising
(RED) for model-based MS image reconstruction. The key ingredient of our
approach is the three-dimensional (3D) deep neural net (DNN) denoiser that can
fully leverage spationspectral correlations within MS images. Our results
suggest the generalizability of our MS-RED algorithm, where a single trained
DNN can be used to solve several different MS imaging problems.
