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

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

1.
福州大学先进制造学院泉州 362251
2.
广东工业大学信息工程学院广州 510006
3.
泉州师范学院信息工程学院泉州 362200

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
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- 被引次数: 0
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

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

1.
School of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, China
2.
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
3.
School of Information Engineering, Quanzhou Normal University, Quanzhou 362200, China

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

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。
- 信号重构 /
- 压缩感知 /
- Dice系数 /
- 正则回溯 /
- 贪婪类算法
Abstract: In order to improve the success rate and reconstruction accuracy of the compressed sensing reconstruction algorithm, the Look Ahead and Regular Backtracking Orthogonal Matching Pursuit based on Dice coefficient (DLARBOMP) is proposed. In this algorithm, from the perspective of matching criteria and atom selection in the pre-selection stage, the Dice coefficient is used to replace the atomic inner product to calculate the correlation value and preserve the characteristics of the original signal, to select the atom that best matches the residual and improve the reconstruction accuracy. At the same time, to reduce backtracking time in the reconstruction process, regularization is used to select multiple atoms instead of a single atom in each iteration, achieving a balance between reconstruction accuracy and time. Finally, the experimental results of sparse one-dimensional signal and two-dimensional image signal reconstruction show that the proposed DLARBOMP algorithm considers both performance and efficiency when reconstructing one-dimensional signal, and enhances the Peak Signal-to-Noise Ratio (PSNR) when reconstructing two-dimensional compressed image signal, as compared to Orthogonal Matching Pursuit (OMP) and the state-of-the-art greedy algorithms.
- Signal reconstruction /
- Compressed sensing /
- Dice coefficient /
- Regular backtracking /
- Greedy algorithms

HTML全文

图 1 压缩感知过程

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图 2 DLARBOMP算法流程

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

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图 4 维信号重构实验

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图 5 重构概率随稀疏度K变化曲线

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图 6 重构概率随观测次数M变化曲线

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图 7 重构时间对比

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图 8 压缩比为0.5时的压缩重构图像

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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
算法	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

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