Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution Imaging

Recently, metasurfaces have experienced revolutionary growth in the sensing and superresolution imaging field, due to their enabling of subwavelength manipulation of electromagnetic waves. However, the addition of metasurfaces multiplies the complexity of retrieving target information from the detec...

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Bibliographic Details
Published in:arXiv.org 2023-05
Main Authors: Zhao, Jin, Huang Zhao Zhang, Ming-Zhe Chong, Yue-Yi, Zhang, Zi-Wen, Zhang, Zong-Kun Zhang, Chao-Hai Du, Pu-Kun Liu
Format: Article
Language:English
Subjects:
Artificial neural networks
Deep learning
Electric fields
Electromagnetic radiation
Image reconstruction
Image resolution
Information retrieval
Interactive systems
Inverse reconstruction
Machine learning
Metasurfaces
Neural networks
Target detection
Teaching methods
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Summary:Recently, metasurfaces have experienced revolutionary growth in the sensing and superresolution imaging field, due to their enabling of subwavelength manipulation of electromagnetic waves. However, the addition of metasurfaces multiplies the complexity of retrieving target information from the detected fields. Besides, although the deep learning method affords a compelling platform for a series of electromagnetic problems, many studies mainly concentrate on resolving one single function and limit the research's versatility. In this study, a multifunctional deep neural network is demonstrated to reconstruct target information in a metasurface targets interactive system. Firstly, the interactive scenario is confirmed to tolerate the system noises in a primary verification experiment. Then, fed with the electric field distributions, the multitask deep neural network can not only sense the quantity and permittivity of targets but also generate superresolution images with high precision. The deep learning method provides another way to recover targets' diverse information in metasurface based target detection, accelerating the progression of target reconstruction areas. This methodology may also hold promise for inverse reconstruction or forward prediction problems in other electromagnetic scenarios.
ISSN:2331-8422