Location

Seattle, WA

Start Date

1-1-1996 12:00 AM

Description

In this paper we present a flaw signature estimation approach which utilizes the Wiener filter [1–5] along with a wavelet based procedure [6–15] to achieve both deconvolution and reduction of acoustic noise. In related ealier work by Patterson et al. [6], the wavelet transform was applied to certain components of the Wiener filter, and coefficient chopping was used to reduce acoustic noise. In the approach that we present here, the wavelet transform is applied individually to the real part and to the imaginary part of the scattering amplitude estimate determined by application of a sub-optimal form of the Wiener filter. This wavelet transform takes the real and imaginary parts, respectively, from the typical Fourier frequency domain to a wavelet phase space. In this new space, the acoustic noise shows significant separation from the flaw signature making selective pruning of wavelet coefficients an effective means of reducing the acoustic noise. The final estimates of the real and imaginary parts of the scattering amplitude are determing via an inverse wavelet transform.

Volume

15A

Chapter

Chapter 3: Signal Processing and Image Analysis

Section

Signal Processing

Pages

733-740

DOI

10.1007/978-1-4613-0383-1_96

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

An Application of Wavelet Signal Processing to Ultrasonic Nondestructive Evaluation

Seattle, WA

In this paper we present a flaw signature estimation approach which utilizes the Wiener filter [1–5] along with a wavelet based procedure [6–15] to achieve both deconvolution and reduction of acoustic noise. In related ealier work by Patterson et al. [6], the wavelet transform was applied to certain components of the Wiener filter, and coefficient chopping was used to reduce acoustic noise. In the approach that we present here, the wavelet transform is applied individually to the real part and to the imaginary part of the scattering amplitude estimate determined by application of a sub-optimal form of the Wiener filter. This wavelet transform takes the real and imaginary parts, respectively, from the typical Fourier frequency domain to a wavelet phase space. In this new space, the acoustic noise shows significant separation from the flaw signature making selective pruning of wavelet coefficients an effective means of reducing the acoustic noise. The final estimates of the real and imaginary parts of the scattering amplitude are determing via an inverse wavelet transform.