| 严凡,林莉,金士杰.超声全矩阵数据联合稀疏重构的多测量向量模型应用[J].,2023,42(3):523-528 |
| 超声全矩阵数据联合稀疏重构的多测量向量模型应用 |
| Joint sparse reconstruction application of Ultrasonic full matrix data using multi-measurement vectors model |
| 投稿时间：2022-02-19 修订日期：2023-04-25 |
| 中文摘要: |
| 针对单测量向量模型(Single Measurement Vector，SMV)等传统压缩感知方法处理超声全矩阵数据时，存在重构精度低和重构耗时长等问题，本文研究了多测量向量模型(Multiple Measurement Vectors，MMV)应用的可行性。针对铝合金试块中不同深度的φ2 mm横通孔，分别使用MMV模型中的多测量稀疏贝叶斯(Multiple Sparse Bayesian Learning，MSBL)算法和SMV模型中的稀疏贝叶斯(Sparse Bayesian Learning，SBL)算法进行超声全矩阵数据重构，并实施全聚焦成像。随后，引入归一化均方误差和阵列性能因子评价图像和信号的重构效果。实验结果表明，SBL算法在25%采样率时的归一化均方误差为1.9%，而MSBL算法仅需15%采样率即可达到相似效果且耗时更少。 |
| 英文摘要: |
| To overcome the low reconstruction accuracy and long reconstruction time for processing ultrasonic full matrix capture data by conventional compressed sensing method, e.g., single measurement vector (SMV) model, this paper studies the feasibility of multiple measurement vectors model (MMV) model. Focusing on the aluminum alloy specimen with φ2 mm side-drilled holes in different depths, the multiple sparse Bayesian learning (MSBL) algorithm in the MMV model and the sparse Bayesian learning (SBL) algorithm in the SMV model were used to reconstruct ultrasound full matrix data and perform total focusing method, respectively. Then, normalized root mean square error and array performance factor were introduced to evaluate the reconstruction effect of images and signals. The experimental results show that the normalized root mean square error for SBL algorithm is 1.9% for 25% sampling rate and the MSBL algorithm only employs 15% sampling rate to achieve similar results with less cost time. |
| DOI：10.11684/j.issn.1000-310X.2023.03.010 |
| 中文关键词: 压缩感知 单测量向量模型 多测量向量模型 全聚焦方法 全矩阵捕捉 |
| 英文关键词: Compressed sensing Single measurement vector Multiple measurement vectors Total focusing method Full matrix capture |
| 基金项目:辽宁省“兴辽英才计划”项目；国家自然科学基金 |
|
| 摘要点击次数: 434 |
| 全文下载次数: 637 |
|
查看全文
查看/发表评论 下载PDF阅读器 |
| 关闭 |
