基于时域线性采样方法的激光超声成像

IF 4.8 2区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Computational Imaging Pub Date : 2025-06-06 DOI:10.1109/TCI.2025.3577405

Jian Song;Fatemeh Pourahmadian;Todd W. Murray;Venkatalakshmi V. Narumanchi

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引用次数: 0

摘要

本文研究了时域线性采样方法（TLSM）应用于有限孔径测量的激光超声（LU）亚表面缺陷层析成像的成像能力。在这种情况下，TLSM指标及其对应的频谱（称为多频LSM）是在LU测试的上下文中制定的。然后，利用合成和实验数据计算了与带有制造缺陷的铝合金试样的LU检测相关的相关成像函数。对反演的超参数进行了计算分析。我们使用合成数据证明，与LSM相比，TLSM指标具有恢复弱（或难以到达的）散射体的独特能力，并且有可能生成更高质量的图像。如果提供高信噪比测量，则可以在从LU测试数据重建中保留这一优势。

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Laser Ultrasonic Imaging Via the Time Domain Linear Sampling Method

This study investigates the imaging ability of the time-domain linear sampling method (TLSM) when applied to laser ultrasonic (LU) tomography of subsurface defects from limited-aperture measurements. In this vein, the TLSM indicator and its spectral counterpart known as the multifrequency LSM are formulated within the context of LU testing. The affiliated imaging functionals are then computed using synthetic and experimental data germane to LU inspection of aluminum alloy specimens with manufactured defects. Hyperparameters of inversion are computationally analyzed. We demonstrate using synthetic data that the TLSM indicator has the unique ability to recover weak (or hard-to-reach) scatterers and has the potential to generate higher quality images compared to LSM. Provided high-SNR measurements, this advantage may be preserved in reconstructions from LU test data.

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来源期刊

IEEE Transactions on Computational Imaging Mathematics-Computational Mathematics

CiteScore

8.20

自引率

7.40%

发文量

期刊介绍： The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.