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Two Dimensional Signal And Image Processing

Applying Compression Recognition Method to Achieve Superresolution of Echo Signals

Ultrasonic inspection of coarse-grained steels is a common challenge in various industrial fields. This task is often difficult because of acoustic scattering that creates structural noise in the ultrasonic signals and images. Therefore, inspections usually use low-frequency probes, which achieve poor resolution with standard delay-and-sum (DAS) imaging techniques, such as the well-known total focusing method (TFM). The purpose of this paper is to evaluate the performance of a super-resolution ultrasonic imaging technique presented by Laroche et al. (IEEE Trans Comput Imaging 7:935–947, 2021) for the inspection of industrial coarse-grained materials. An image deconvolution problem (with spatially varying blur) is formulated, relying on a forward model that links the TFM image to the acoustic reflectivity map. The experiments consider an austenitic-ferritic stainless steel sample insonified using array probes at 3 MHz and 5 MHz placed in contact. The goal is to resolve two close reflectors corresponding to side-drilled holes (SDH) with diameter 0.4 mm spaced by 0.4 mm edge-to-edge and positioned at different depths (10, 20, 30, 40 mm). This configuration corresponds to a critical case where the distance between the two reflectors is much lower than the Rayleigh distance, that is the resolution limit of a DAS imaging system. These are typical cases where DAS images obtained from low-frequency inspections critically lack resolution, but where higher frequency probes cannot be used in practice, because a too week signal-to-noise ratio would affect the detection capability. As predicted by the Rayleigh criterion, TFM is not able to separate the reflectors. The proposed image reconstruction method successfully resolves the majority of the reflectors with a rather accurate distance estimation. In the context of coarse-grained structure inspection, subwavelength reflectors distant from each other by two times less than the resolution limit given by the Rayleigh criterion have been successfully detected and separated. This approach hence enables the use of low-frequency probes, in order to improve the signal-to-noise ratio, while keeping high resolution capability which can be particularly interesting for industrial applications. In particular, the proposed approach shows promising results for the sizing of a real crack in an industrial sample. 

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Detection and Separation of Close Flaws in Coarse-Grained Materials Using Ultrasonic Image Deconvolution

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).

Marcelo Victor

Papers

A Sparse Reconstruction Algorithm for Ultrasonic Images in