摘要翻译：
单面超声无损检测(UNDE)被广泛应用于检测和维护结构的缺陷和缺陷，这些缺陷和缺陷可能会影响核电厂的性能，如核电厂。大多数UNDE系统将声脉冲发送到感兴趣的结构中，测量接收到的波形，并使用算法重建感兴趣的数量。合成孔径聚焦技术(SAFT)是UNDE系统中应用最广泛的算法，因为它能实时产生可接受的结果。已有的几种线性模型正则化反演技术可以改进SAFT的反演方法，但它们往往作出简化的假设，没有解决如何从大型真实数据集中获得重建的问题。本文提出了一种基于模型的迭代重建算法(MBIR)。为了进一步减少结果中的一些伪影，我们对前向模型进行了改进，以考虑发射波束的分布、直接到达信号的出现以及相邻区域扫描之间的相关性。接下来，我们将前向模型与空间变化的先验模型结合起来，来考虑更深区域的衰减。我们还提出了一种从大数据集联合重建测量值的算法。最后，利用模拟和大量的实验数据，我们展示了MBIR的结果，并证明了我们如何改进SAFT和现有的正则化反演技术。
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英文标题：
《Model-Based Iterative Reconstruction for One-Sided Ultrasonic
  Non-Destructive Evaluation》
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作者：
Hani Almansouri and Singanallur Venkatakrishnan and Charles Bouman and
  Hector Santos-Villalobos
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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英文摘要：
  One-sided ultrasonic non-destructive evaluation (UNDE) is extensively used to characterize structures that need to be inspected and maintained from defects and flaws that could affect the performance of power plants, such as nuclear power plants. Most UNDE systems send acoustic pulses into the structure of interest, measure the received waveform and use an algorithm to reconstruct the quantity of interest. The most widely used algorithm in UNDE systems is the synthetic aperture focusing technique (SAFT) because it produces acceptable results in real time. A few regularized inversion techniques with linear models have been proposed which can improve on SAFT, but they tend to make simplifying assumptions that do not address how to obtain reconstructions from large real data sets. In this paper, we propose a model-based iterative reconstruction (MBIR) algorithm designed for scanning UNDE systems. To further reduce some of the artifacts in the results, we enhance the forward model to account for the transmitted beam profile, the occurrence of direct arrival signals, and the correlation between scans from adjacent regions. Next, we combine the forward model with a spatially variant prior model to account for the attenuation of deeper regions. We also present an algorithm to jointly reconstruct measurements from large data sets. Finally, using simulated and extensive experimental data, we show MBIR results and demonstrate how we can improve over SAFT as well as existing regularized inversion techniques.
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PDF链接：
https://arxiv.org/pdf/1808.03336