Comparison of cascade and concurrent signal denoising methods

An important issue in ultrasonic nondestructive testing is the detection of flaw echoes in the presence of background noise created by instrumentation and by clutter noise. Signal averaging, autoregressive analysis, spectrum analysis, matched filtering and the wavelet transform have all been used to filter noise in ultrasonic signals. Widely-used wavelet threshold estimation algorithms are not designed for EMAT pulse-echo signals, and therefore do not exploit their unique impulse nature. The approaches to ultrasonic signal filtering proposed in this paper are based on wavelet denoising combined with other denoising algorithms. The denoising algorithms are combined with a knowledge ultrasonic echo detection and system frequency characteristics. The compared methods were evaluated on signals measured with EMAT probes and under various Signal-to-Noise Ratio (SNR) conditions, and they outperform the wavelet transform with the UNIVERSAL threshold estimation method. The results indicated SNR enhancement up to 60 dB with real EMAT data.