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Open AccessJournal ArticleDOI

A sparse reconstruction algorithm for ultrasonic images in nondestructive testing.

TLDR
In this paper, an image reconstruction algorithm based on regularized least squares using a l 1 regularization norm was proposed to reconstruct an image of a point-like reflector, using both simulated and real data.
Abstract
Ultrasound imaging systems (UIS) are essential tools in nondestructive testing (NDT). In general, the quality of images depends on two factors: system hardware features and image reconstruction algorithms. This paper presents a new image reconstruction algorithm for ultrasonic NDT. The algorithm reconstructs images from A-scan signals acquired by an ultrasonic imaging system with a monostatic transducer in pulse-echo configuration. It is based on regularized least squares using a l1 regularization norm. The method is tested to reconstruct an image of a point-like reflector, using both simulated and real data. The resolution of reconstructed image is compared with four traditional ultrasonic imaging reconstruction algorithms: B-scan, SAFT, !-k SAFT and regularized least squares (RLS). The method demonstrates significant resolution improvement when compared with B-scan—about 91% using real data. The proposed scheme also outperforms traditional algorithms in terms of signal-to-noise ratio (SNR).

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Journal ArticleDOI

Sizing of flaws using ultrasonic bulk wave testing: A review

TL;DR: Techniques that utilise ultrasonic bulk waves to size flaws, including amplitude, temporal, imaging and inversion, are reviewed.
Journal ArticleDOI

Frequency Optimization for Enhancement of Surface Defect Classification Using the Eddy Current Technique

TL;DR: An investigation into optimum excitation frequency has been conducted to enhance surface defect classification performance and it is observed that probe signals are the most sensitive on the whole for a group of defects whenexcitation frequency is set near the frequency at which maximum probe signal signals are retrieved for the largest defect.
Journal ArticleDOI

An Inverse Approach for Ultrasonic Imaging From Full Matrix Capture Data: Application to Resolution Enhancement in NDT

TL;DR: A linear model is built that links the FMC data, i.e., the signals collected from all transmitter–receiver pairs of an ultrasonic array, to the discretized reflectivity map of the inspected object, which includes the ultrasonic waveform corresponding to the response of transducers.
Journal ArticleDOI

Accurate 3D reconstruction of bony surfaces using ultrasonic synthetic aperture techniques for robotic knee arthroplasty

TL;DR: The ability of a robotic deployed ultrasound imaging system based on synthetic aperture methods to accurately reconstruct bony surfaces is investigated, demonstrating the feasibility of the approach to deliver the sub-millimetre accuracy required for the application.
Journal ArticleDOI

Model-Based Iterative Reconstruction for One-Sided Ultrasonic Nondestructive Evaluation

TL;DR: This paper proposes a model-based iterative reconstruction (MBIR) algorithm designed for scanning UNDE systems and shows how it can improve over SAFT as well as existing regularized inversion techniques.
References
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Book

Convex Optimization

TL;DR: In this article, the focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them, and a comprehensive introduction to the subject is given. But the focus of this book is not on the optimization problem itself, but on the problem of finding the appropriate technique to solve it.
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Nonlinear total variation based noise removal algorithms

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Journal ArticleDOI

For most large underdetermined systems of linear equations the minimal 1-norm solution is also the sparsest solution

TL;DR: In this article, the authors consider linear equations y = Φx where y is a given vector in ℝn and Φ is a n × m matrix with n 0 so that for large n and for all Φ's except a negligible fraction, the solution x1of the 1-minimization problem is unique and equal to x0.
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Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion

TL;DR: In this article, the authors present a survey of regularization tools for rank-deficient problems and problems with ill-conditioned and inverse problems, as well as a comparison of the methods in action.
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