.png){kind=icon}
返回
Phase Retrieval: From Computational Imaging to Machine Learning: A tutorial
.png){kind=icon}
OA
AI
DOI：10.1109/MSP.2022.3219240.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Machine learning algorithms
Phase measurement
Computational modeling
Neural networks
Signal processing algorithms
Tutorials
Reconstruction algorithms
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
分享收藏Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications在计算成像中集成物理和学习模型的即插即用方法: 理论，算法和应用OAAI
OAAI分享收藏Wide-field, high-resolution Fourier ptychographic microscopy宽视场高分辨率傅里叶印刷显微镜.png)
NATURE PHOTONICSOAAI
NATURE PHOTONICSIF32.9
OAAI分享收藏Lensless digital holographic microscopy and its applications in biomedicine and environmental monitoringMETHODS
OAAI
METHODSIF4.3
OAAI分享收藏
查看更多内容