Phase Retrieval Using Double Deep Image Priors

Zhong Zhuang, David Yang, David Barmherzig, Ju Sun

Keywords: Phase Retrieval, BCDI, Single-Instance Deep Learning

Abstract:

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

Submission Number: 33