Adaptive thresholding technique for denoising ultrasonic signals

Abstract

In many ultrasonic imaging applications, the signal acquired is embedded in noise, and situations with very small signal-to-noise ratio (SNR) are not uncommon. Thus, before any data analysis can be applied to the signal some level of noise removal is necessary. In this paper, we analyze the denoising performance of the discrete wavelet transform (DWT), discrete cosine transform (DCT), and Walsh-Hadamard transform (WHT) using an adaptive thresholding function (ATF) that, when applied to DWT, DCT and WHT coefficients, improves the signal-to-noise (SNR) of ultrasonic signals embedded in noise. In particular, the ATF technique is successful in denoising low SNR ultrasonic signals. Furthermore, the ATF approach outperforms the classical techniques when the ultrasonic signal has a low SNR (below 5dB). For signals with uniform noise added to DWT coefficients, the ATF technique achieves SNR improvements around 9dB over the classical thresholding techniques; these improvements are above 10dB for Gaussian noise. �0.5 �0.4 �0.3 �0.2 �0.1 0 0.1 0.2 0.3 0.4 0.5 �0.5 �0.4 �0.3 �0.2 �0.1 0 0.1 0.2 0.3 0.4 0.5