Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

doi:10.1109/MSP.2022.3219240

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

2023-01-01

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DOI：10.1109/MSP.2022.3219240

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Phase retrieval consists in the recovery of a complex-valued signal from intensity-only measurements. As it pervades a broad variety of applications, many researchers have striven to develop phase-retrieval algorithms. Classical approaches involve techniques as varied as generic gradient descent routines or specialized spectral methods, to name a few. However, the phase-recovery problem remains a challenge to this day. Recently, however, advances in machine learning have revitalized the study of phase retrieval in two ways: 1) significant theoretical advances have emerged from the analogy between phase retrieval and single-layer neural networks, and 2) practical breakthroughs have been obtained thanks to deep learning regularization. In this tutorial, we review phase retrieval under a unifying framework that encompasses classical and machine learning methods. We focus on three key elements: applications, an overview of recent reconstruction algorithms, and the latest theoretical results.

Keywords:

Machine learning algorithms

Phase measurement

Computational modeling

Neural networks

Signal processing algorithms

Tutorials

Reconstruction algorithms

Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial

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