CUP-VISAR image reconstruction based on iterative-interframe double prediction

Wen Yilan; Li Haiyan; Gan Huaquan; Huang Yunbao; Wang Feng; Li Yulong; Guan Zanyang; Yu Yuanping; Huang Qingxin; Zheng Kaitao

doi:10.11884/HPLPB202537.240247

Volume 37 Issue 2

Feb. 2025

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Wen Yilan, Li Haiyan, Gan Huaquan, et al. CUP-VISAR image reconstruction based on iterative-interframe double prediction[J]. High Power Laser and Particle Beams, 2025, 37: 022002. doi: 10.11884/HPLPB202537.240247

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Wen Yilan, Li Haiyan, Gan Huaquan, et al. CUP-VISAR image reconstruction based on iterative-interframe double prediction[J]. High Power Laser and Particle Beams, 2025, 37: 022002. doi: 10.11884/HPLPB202537.240247

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CUP-VISAR image reconstruction based on iterative-interframe double prediction

doi: 10.11884/HPLPB202537.240247

1.
School of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China
2.
Laser Fusion Research Center, CAEP, Mianyang 621900, China

Received Date: 2024-08-03
Accepted Date: 2024-12-23
Rev Recd Date: 2024-12-20

Available Online: 2025-01-13

Publish Date: 2025-02-15

Abstract

Abstract

CUP-VISAR system is a new technology that combines Compressed Ultrafast Photography (CUP) with two-dimensional Velocity Interferometer System for Any Reflector (VISAR). To solve the problem that the image reconstruction quality of CUP-VISAR system decreases obviously under the condition of large noise, a compressed ultrafast photography reconstruction method based on iteration-interframe dual prediction is proposed. Using this method, the correlation of inter-frame image data and the correlation of iterations before and after the same frame image are studied. The compressed image reconstruction problem is presented as an iteration-inter frame dual prediction optimization problem based on Kalman prediction and inter-frame prediction, and the Plug-and-Play Generalized Alternating Projection (PnP-GAP) framework is used to solve the optimization problem effectively. Simulation results show that the minimum Peak Signal-to-Noise Ratio (PSNR) and minimum Structure Similarity Index Measure (SSIM) of the proposed method are increased by 3.18−2.11 dB and 20.30%−8.22% under large Gaussian noise conditions. The practical results show that the proposed method can obtain higher definition of fringe image, and the reconstructed line-VISAR (1D-VISAR) fringe movement trend is clearer, which verifies the effectiveness of the algorithm.
- CUP-VISAR,
- interframe prediction,
- Kalman prediction,
- plug-and-play generalised alternating projection,
- image reconstruction

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References(31)

References

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