Comparing Plug-and-Play and Unrolled networks
Abstract
Plug-and-play and unrolled methods are among the most popular and efficient approaches for solving inverse problems in imaging. In this paper, we review their similarities and differences. We adopt a Bayesian framework to explain their statistical properties. This allows us to relate plug-andplay methods to Maximum A Posteriori (MAP) estimators and unrolled networks to Minimum Mean Square Error (MMSE) estimators. We clarify some of their properties including their stability and numerical efficiency. Overall, this paper provides a concise review of their respective advantages and limitations.
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