End-to-end learning for phase retrieval

Raunak Manekar

Abstract:

We consider the end-to-end deep learning approach for phase retrieval, a central problem in
scientific imaging. We highlight a fundamental
difficulty for learning that previous work has neglected, likely due to the biased datasets they use
for training and evaluation. We propose a simple yet different formulation for PR that seems
to overcome the difficulty and return consistently
better qualitative results.