Research on algorithm for restoration of large aperture and thick pinhole imaging based on neural network

Li Dong; Sheng Liang; Li Yang; Duan Baojun

doi:10.11884/HPLPB202234.210345

Volume 34 Issue 6

Apr. 2022

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Article Navigation > High Power Laser and Particle Beams > 2022 > 34(6): 064002

Li Dong, Sheng Liang, Li Yang, et al. Research on algorithm for restoration of large aperture and thick pinhole imaging based on neural network[J]. High Power Laser and Particle Beams, 2022, 34: 064002. doi: 10.11884/HPLPB202234.210345

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Li Dong, Sheng Liang, Li Yang, et al. Research on algorithm for restoration of large aperture and thick pinhole imaging based on neural network[J]. High Power Laser and Particle Beams, 2022, 34: 064002. doi: 10.11884/HPLPB202234.210345

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Research on algorithm for restoration of large aperture and thick pinhole imaging based on neural network

doi: 10.11884/HPLPB202234.210345

State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China

Received Date: 2021-08-09
Rev Recd Date: 2021-12-21

Available Online: 2021-12-13

Publish Date: 2022-06-15

Abstract

Abstract

To obtain the spatial distribution image of low intensity radiation source better, a method is proposed to restore large aperture thick pinhole degraded image using neural network algorithm. The thick pinhole model of 5 mm, 10 mm and 15 mm apertures is established, and the degenerate image sets of thick pinhole for the shape radiation source of 3600 Chinese characters are obtained. Based on the DnCNN neural network model, the neural network for image restoration with large aperture and thick pinhole is obtained, and compared with traditional algorithms such as Wiener filter and Lucy Richardson. After considering the influence of noise, the original neural network model is trained by means of transfer learning theory, and then the degraded image of large aperture pinhole with noise is restored. The RMSE of neural network algorithm is significantly lower than that of the traditional one, and the effect of noise is greatly improved by transfer learning. This paper proves the superiority of neural network algorithm in the field of image restoration with large aperture and thick pinhole, and verifies the feasibility of neural network method to restore the large aperture thick pinhole degraded image with noise.
- large thick aperture,
- neural network,
- image reconstruction,
- transfer learning

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References(12)

References

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