Phase Retrieval Using Double Deep Image Priors

Phase retrieval (PR) concerns the recovery of complex phases from complexmagnitudes. We identify the connection between the difficulty level and thenumber and variety of symmetries in PR problems. We focus on the most difficultfar-field PR (FFPR), and propose a novel method using double deep image priors.In realistic evaluation, our method outperforms all competing methods by largemargins. As a single-instance method, our method requires no training data andminimal hyperparameter tuning, and hence enjoys good practicality.