计算机科学
反问题
概化理论
医学影像学
人工智能
大数据
深度学习
光学(聚焦)
机器学习
电磁学
约束(计算机辅助设计)
集合(抽象数据类型)
数据集
数据科学
数据挖掘
物理
数学
统计
光学
数学分析
工程物理
程序设计语言
几何学
作者
Rui Guo,Tianyao Huang,Maokun Li,Haiyang Zhang,Yonina C. Eldar
出处
期刊:IEEE Signal Processing Magazine
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:40 (2): 18-31
被引量:7
标识
DOI:10.1109/msp.2022.3198805
摘要
Electromagnetic (EM) imaging is widely applied in sensing for security, biomedicine, geophysics, and various industries. It is an ill-posed inverse problem whose solution is usually computationally expensive. Machine learning (ML) techniques and especially deep learning (DL) show potential in fast and accurate imaging. However, the high performance of purely data-driven approaches relies on constructing a training set that is statistically consistent with practical scenarios, which is often not possible in EM-imaging tasks. Consequently, generalizability becomes a major concern. On the other hand, physical principles underlie EM phenomena and provide baselines for current imaging techniques. To benefit from prior knowledge in big data and the theoretical constraint of physical laws, physics-embedded ML methods for EM imaging have become the focus of a large body of recent work.
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