Abstract

An efficient procedure for experimental-based quantification of statistical distributions of both the random and microstructural speckle noise within an ultrasonic image is presented. This is of particular interest in the multiview total focusing method, which enables many images (views) of the same region to be obtained by utilizing alternative ray paths and mode conversions. For example, in an immersion configuration, 21 separate views of the same region of a sample can be formed by exploiting direct and skip paths. These views can be combined through some form of data fusion algorithm to improve defect detection and characterization performance. However, the noise level is different in different views and this should be accounted for in any data fusion algorithm. It is shown that by using only one set of experimental data from a single measurement location, rather than numerous independent locations, it is possible to obtain accurate noise parameters at an imaging level. This is achieved by accounting for the spatial variation in the noise parameters within the image, due to beam spread, directivity, and attenuation with a simple empirical correction. An important feature of the process is the suppression of image artifacts caused by signal responses from other ray paths with the use of image masking. This masking process incorporates knowledge of the expected autocorrelation length (ACL) of image speckle noise and high-amplitude cluster suppression. The expected ACL is determined via a simple ray-based forward model of a single point scatterer. Compared to the estimates obtained using multiple independent locations, the speckle noise parameters estimated from a single measurement location were within 0.4 dB.

Highlights

  • U LTRASONIC inspection is utilized to detect and characterize defects within a structure in order to ensure the standard of manufacture and protect against failures over its lifetime

  • Since the random noise suppression required cannot be assessed prior to calculation of speckle noise, it is recommended that a conservative choice for the number of averages is made at this stage, e.g., 10-20

  • To determine the random noise, only a pair of full matrix capture (FMC) data sets captured in succession is required

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Summary

BACKGROUND

U LTRASONIC inspection is utilized to detect and characterize defects within a structure in order to ensure the standard of manufacture and protect against failures over its lifetime. In FMC, A-scan data from all possible combinations of transmitter and receiver elements from the array are recorded individually This allows for separation between the data capture procedure and the imaging approach, as no physical steering or focusing is undertaken. The focus of this paper is quantifying the noise levels and associated statistical distributions present in different TFM images from different views It is a necessary step for determining the inspection limit for any conventional array inspection as well as in providing reliable signal-to-noise estimates for data fusion.

EXPERIMENTAL CONFIGURATION AND MULTIVIEW TFM IMAGING METHOD
IMAGE NOISE CHARACTERIZATION
Random Noise
Coherent Grain Noise
Image Feature Detection
SUMMARY OF PROPOSED NOISE CHARACTERIZATION PROCEDURE FOR MULTIVIEW TFM IMAGES
Findings
CONCLUSION
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