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基于迭代-帧间双预测的CUP-VISAR重构方法

温懿岚 李海艳 甘华权 黄运保 王峰 理玉龙 关赞洋 余远平 黄庆鑫 郑铠涛

温懿岚, 李海艳, 甘华权, 等. 基于迭代-帧间双预测的CUP-VISAR重构方法[J]. 强激光与粒子束, 2025, 37: 022002. doi: 10.11884/HPLPB202537.240247
引用本文: 温懿岚, 李海艳, 甘华权, 等. 基于迭代-帧间双预测的CUP-VISAR重构方法[J]. 强激光与粒子束, 2025, 37: 022002. doi: 10.11884/HPLPB202537.240247
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
Citation: 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

基于迭代-帧间双预测的CUP-VISAR重构方法

doi: 10.11884/HPLPB202537.240247
基金项目: 国家自然科学基金项目(12127810、51975125)
详细信息
    作者简介:

    温懿岚,1193556755@qq.com

    通讯作者:

    李海艳,cathylhy@gdut.edu.cn

  • 中图分类号: TP391

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

  • 摘要: CUP-VISAR系统是将超快压缩成像(CUP)与二维任意反射面速度干涉仪(VISAR)结合的新技术。针对CUP-VISAR系统在含有大噪声情况下图像重构质量明显下降的问题,提出了一种基于迭代-帧间双预测的压缩超快摄影重构方法。对帧间图像数据的相关性及同一帧图像前后迭代的关联性进行研究,将压缩图像重构问题表述为一个基于卡尔曼预测和帧间预测的迭代-帧间双预测优化问题,并使用即插即用广义交替投影(PnP-GAP)框架来有效解决优化问题。仿真实验表明,在大高斯噪声条件下,所提方法的最小峰值信噪比(PSNR)提高了3.18~2.11 dB,最小结构相似性(SSIM)提高了20.30%~8.22%。实际结果表明,所提方法得到的条纹图像清晰度更高,重构的线-VISAR(1D-VISAR)条纹移动趋势更清晰,验证了算法的有效性。
  • 图  1  CUP-VISAR系统示意图

    Figure  1.  Schematic diagram of CUP-VISAR system

    图  2  帧间预测过程

    Figure  2.  Interframe prediction process

    图  3  所提方法的流程图

    Figure  3.  Flow chart of the proposed method

    图  4  模拟实验观测数据

    Figure  4.  Simulated experimental observation data

    图  5  不同算法的重构结果

    Figure  5.  Reconstruction results of different algorithms

    图  6  重构图像的PSNR和SSIM图

    Figure  6.  PSNR and SSIM plots of reconstructed images

    图  7  实验观测数据

    Figure  7.  Experimental observations

    图  8  每种算法的重构条纹结果

    Figure  8.  Inversion fringes of each algorithm

    图  9  第25帧重构局部对比

    Figure  9.  Local comparison between frame 25 inversion of different algorithms

    图  10  从重构图像中提取1D-VISAR图

    Figure  10.  Extracted 1D-VISAR image from the inversion images

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出版历程
  • 收稿日期:  2024-08-03
  • 修回日期:  2024-12-20
  • 录用日期:  2024-12-23
  • 网络出版日期:  2025-01-13
  • 刊出日期:  2025-02-15

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