Phase Retrieval Using Double Deep Image Priors

Published: 03 Nov 2023, Last Modified: 03 Nov 2023NeurIPS 2023 Deep Inverse Workshop PosterEveryoneRevisionsBibTeX
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
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