Feasibility-based Fixed Point Networks

Understand

Inverse problems consist of recovering a signal from a collection of noisy measurements.

• These problems can often be cast as feasibility problems; however, additional regularization is typically necessary to ensure accurate and stable recovery with respect to data perturbations.
• Hand-chosen analytic regularization can yield desirable theoretical guarantees, but such approaches have limited effectiveness recovering signals due to their inability to leverage large amounts of available data.
• To this end, this work fuses data-driven regularization and convex feasibility in a theoretically sound manner.