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

Infusing Learned Priors into Model-Based Multispectral Imaging