Optimal wavelet estimation for data compression and noise suppression of ultrasonic NDE signals

Ultrasonic NDE applications often require a significant amount of data collection, storage, and in some instances data transmission and analysis. The analysis, storage, and transmission of ultrasonic data can benefit from compression and noise suppression algorithms. In this paper, effort has been focused on the determination of similarities between the wavelet kernel and the ultrasonic echoes to maximize data compression ratios. The design of wavelet kernels via optimization methods improves data compression and denoising. The influence of thresholding into data compression and denoising are investigated. Simulated data, as well as ultrasonic experimental signals are used to verify the results of this study. High data compression ratios and echo detection in very low signal-to-noise ratio is achieved with this approach.