Research on blind recovery method of wideband electromagnetic image convolutional neural network

Zhu Yanju; Xie Shuguo; Li Yuanhao; Zhang Xian

doi:10.11884/HPLPB201931.190191

Volume 31 Issue 10

Oct. 2019

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Zhu Yanju, Xie Shuguo, Li Yuanhao, et al. Research on blind recovery method of wideband electromagnetic image convolutional neural network[J]. High Power Laser and Particle Beams, 2019, 31: 103210. doi: 10.11884/HPLPB201931.190191

Citation:

Zhu Yanju, Xie Shuguo, Li Yuanhao, et al. Research on blind recovery method of wideband electromagnetic image convolutional neural network[J]. High Power Laser and Particle Beams, 2019, 31: 103210. doi: 10.11884/HPLPB201931.190191

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Research on blind recovery method of wideband electromagnetic image convolutional neural network

doi: 10.11884/HPLPB201931.190191

EMC Research Institute, Electronic Information Engineering College, Beihang University, Beijing 100191, China

Received Date: 2019-05-30
Rev Recd Date: 2019-06-17
Publish Date: 2019-10-15

Abstract

Abstract

In the process of electromagnetic interference sources imaging testing using parabolic reflection, the diffraction phenomenon of the system leads to blurred and low resolution images. Interference sources have different resolution capabilities in different areas and frequencies, so it's difficult to enhance image resolution by using existed super-resolution algorithm. In order to realize the blind recovery of wideband electromagnetic images, a method based on convolutional neural network is proposed. Network training is the process which directly inputs a blurred image and reconstructs a high quality image without assuming any particular blur and noise model. Both experiment and simulation results demonstrate that the convolutional neural network blind recovery method outperforms other blind recovery calculations in different imaging regions of wide frequency band.
- convolutional neural network,
- electromagnetic imaging,
- blind recovery,
- electromagnetic interference source

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References(11)

References

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