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Dice系数前向预测的快速正交正则回溯匹配追踪算法

陈平平 陈家辉 王宣达 方毅 王锋

陈平平, 陈家辉, 王宣达, 方毅, 王锋. Dice系数前向预测的快速正交正则回溯匹配追踪算法[J]. 电子与信息学报, 2024, 46(4): 1488-1498. doi: 10.11999/JEIT230558
引用本文: 陈平平, 陈家辉, 王宣达, 方毅, 王锋. Dice系数前向预测的快速正交正则回溯匹配追踪算法[J]. 电子与信息学报, 2024, 46(4): 1488-1498. doi: 10.11999/JEIT230558
CHEN Pingping, CHEN Jiahui, WANG Xuanda, FANG Yi, WANG Feng. Regular Backtracking Fast Orthogonal Matching Pursuit Algorithm Based on Dice Coefficient Forward Prediction[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1488-1498. doi: 10.11999/JEIT230558
Citation: CHEN Pingping, CHEN Jiahui, WANG Xuanda, FANG Yi, WANG Feng. Regular Backtracking Fast Orthogonal Matching Pursuit Algorithm Based on Dice Coefficient Forward Prediction[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1488-1498. doi: 10.11999/JEIT230558

Dice系数前向预测的快速正交正则回溯匹配追踪算法

doi: 10.11999/JEIT230558
基金项目: 国家自然科学基金(62171135,62071131),福建省杰青项目(2022J06010),省教育厅重点攻关项目(2023XQ004),泉州市科技计划项目(2021N050)
详细信息
    作者简介:

    陈平平:男,博士,教授,研究方向为压缩感知、无线通信、信道编码调制、多用户接入

    陈家辉:男,硕士生,研究方向为压缩感知、无线通信、多用户接入

    王宣达:男,硕士生,研究方向为压缩感知、无线通信、多用户接入

    方毅:男,博士,教授,研究方向为信道纠错编码与调制、无线通信

    王锋:男,博士,教授,研究方向为电子与通信技术

    通讯作者:

    方毅 fangyi@gdut.edu.cn

  • 中图分类号: TN911.23

Regular Backtracking Fast Orthogonal Matching Pursuit Algorithm Based on Dice Coefficient Forward Prediction

Funds: The National Natural Science Foundation of China (62171135, 62071131), Fujian Distinguished Talent Project (2022J06010), The Key Project of Education Department (2023XQ004), Quanzhou Sci-Tech Project (2021N050)
  • 摘要: 为了提高压缩感知重构算法的成功率与重构精度,该文提出基于Dice前向预测的正交正则回溯匹配追踪算法 (DLARBOMP)。在该算法中,首先从匹配准则与预选阶段原子选取的角度,利用Dice系数代替原子内积计算相关度,保留原始信号信息的特性,以此选择与残差最匹配的原子,提高算法的重构精度。同时,针对信号重构过程回溯算法的时间过长问题,在每次原子迭代过程中,该文利用正则化选择多个原子而非单个原子,实现重构精度与重构时间的平衡。最后,通过稀疏1维信号与2维图像信号重构的实验结果,显示了所提DLARBOMP算法在1维信号重构时兼顾了性能与效率,在2维压缩图像信号重构时提高其峰值信噪比(PSNR),优于正交匹配追踪(OMP)及其最新改进贪婪类算法。
  • 图  1  压缩感知过程

    图  2  DLARBOMP算法流程

    图  3  N = 256, K = 20, M=80残差值随迭代次数的变化关系

    图  4  维信号重构实验

    图  5  重构概率随稀疏度K变化曲线

    图  6  重构概率随观测次数M变化曲线

    图  7  重构时间对比

    图  8  压缩比为0.5时的压缩重构图像

    表  1  压缩比${M \mathord{\left/ {\vphantom {M N}} \right. } N}$分别为0.3,0.5和0.7时算法重构性能比较

    算法 0.3 0.5 0.7
    PSNR(dB) $\sigma $ PSNR(dB) $\sigma $ PSNR(dB) $\sigma $
    OMP[10] 20.1031 0.1394 26.3683 0.0902 29.9089 0.0574
    LAOMP[14] 21.9710 0.1377 26.8765 0.0847 30.3367 0.0559
    LABOMP[15] 21.9811 0.1370 27.0628 0.0835 30.3590 0.0554
    MMP-IIPMC[28] 22.0009 0.1318 27.1078 0.0812 31.0811 0.0511
    MPSP[27] 21.9251 0.1301 26.8977 0.0841 30.3806 0.0557
    DWBMP[23] 21.8674 0.1335 26.9524 0.0836 30.3441 0.0552
    本文DLARBOMP 22.0072 0.1296 27.2606 0.0810 31.7642 0.0473
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-06-10
  • 修回日期:  2023-09-22
  • 网络出版日期:  2023-10-18
  • 刊出日期:  2024-04-24

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