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Image domain prior models have been shown to improve the quality ofreconstructed images, especially when data are limited. Pre-processing of rawdata, through the implicit or explicit inclusion of data domain priors haveseparately also shown utility in improving reconstructions. In this work, aprincipled approach is presented allowing the unified integration of both dataand image domain priors for improved image reconstruction. The consensusequilibrium framework is extended to integrate physical sensor models, datamodels, and image models. In order to achieve this integration, theconventional image variables used in consensus equilibrium are augmented withvariables representing data domain quantities. The overall result producescombined estimates of both the data and the reconstructed image that isconsistent with the physical models and prior models being utilized. The priormodels used in both domains in this work are created using deep neuralnetworks. The superior quality allowed by incorporating both data and imagedomain prior models is demonstrated for two applications: limited-angle CT andaccelerated MRI. The prior data model in both these applications is focused onrecovering missing data. Experimental results are presented for a 90 degreelimited-angle tomography problem from a real checked-bagged CT dataset and a 4xaccelerated MRI problem on a simulated dataset. The new framework is veryflexible and can be easily applied to other computational imaging problems withimperfect data.

Data and Image Prior Integration for Image Reconstruction Using Consensus Equilibrium