Integrating Learned Data and Image Models Through Consensus Equilibrium For Model-Based Image Reconstruction

Model-based image reconstruction (MBIR) approaches provide a principled way to combine sensor and image prior models to solve imaging inverse problems. Plug-and-play MBIR (PnP-MBIR) approaches use variable splitting and allow the use of rich image priors. This work extends this idea to data-domain modeling and presents a new MBIR framework that enables integration of data-domain and image-domain prior models and allows high-quality reconstructions even for imperfect data imaging problems such as limited-angle CT, accelerated MRI, or diverging-wave Ultrasound imaging. In this work, we use this newly developed framework to explore the potential of learned data and image models for biomedical applications of tomographic imaging problems.