文献类型：期刊文献

英文题名：Discretized tensor-based model of total focusing method: A sparse regularization approach for enhanced ultrasonic phased array imaging

作者：Zhao, Zhiyuan[1];Liu, Lishuai[1];Liu, Wen[1];Teng, Da[1];Xiang, Yanxun[1];Xuan, Fu-Zhen[1]

机构：[1]East China Univ Sci & Technol, Sch Mech & Power Engn, Shanghai Key Lab Intelligent Sensing & Detect, Shanghai 200237, Peoples R China

年份：2024

卷号：141

外文期刊名：NDT & E INTERNATIONAL

收录：;EI(收录号：20234715092896);WOS:【SCI-EXPANDED(收录号:WOS:001118889900001)】；

基金：This work was supported by the National Key Research and Devel-opment Program of China (Grant No. 2022YFF0605600) , the National Natural Science Foundation of China (Grant Nos. 12374434, 12025403 and 12104155) , and the Shanghai Chenguang Program (Grant No. 21CGA36) .

语种：英文

外文关键词：TFM; FMC; Discretized tensor-based model; Sparse regularization; ReLU-FISTA

摘要：The total focusing method (TFM) is considered as the standard in ultrasonic phased array imaging and plays a vital role in industrial non-destructive testing (NDT). By utilizing the full matrix capture (FMC) dataset, the TFM can focus on every point within the specified imaging region, and is more accurate than the traditional ultrasonic phased array imaging methods. However, the TFM is essentially a delay and sum technique that often operates linearly on the time-domain signals and takes no prior information into account, so its image quality remains inadequate when dealing with defects in close proximity or scattering materials. To address this problem, this paper formulates the imaging principle of the TFM as a Boolean matrix and establishes the discretized tensorbased model. Subsequently, the model is addressed by employing the sparse regularization strategy, taking into some characteristics of industrial NDT. Regarding the solution algorithm, due to the generation of negative values by the fast iterative shrinkage threshold algorithm (FISTA), this paper introduces the rectified linear unit (ReLU) function as a non-negative constraint and presents a dedicated solution algorithm (ReLU-FISTA) for acquiring detection results. Through verification of simulation and experiment, the proposed approach exhibits superior capabilities of defect characterization and noise suppression when compared to the TFM, leading to substantial enhancements in image quality.