Résumé
Ultrasound imaging is one of the most widely used non-destructive testing methods. The transducer emits pulses that travel through the imaged samples and are reflected by echo-forming impedance. The resulting ultrasonic signals usually contain noise. Most of the traditional noise reduction algorithms require high skills and prior knowledge of noise distribution, which has a crucial impact on their performances. As a result, these methods generally yield a loss of information, significantly influencing the final data and deeply limiting both sensitivity and resolution of imaging devices in medical and industrial applications. In the present study, a denoising method based on an attention-gated convolutional autoencoder is proposed to fill this gap. To evaluate its performance, the suggested protocol is compared to widely used methods such as butterworth filtering (BF), discrete wavelet transforms (DWT), principal component analysis (PCA), and convolutional autoencoder (CAE) methods. Results proved that better denoising can be achieved especially when the original signal-to-noise ratio (SNR) is very low and the sound waves’ traces are distorted by noise. Moreover, the initial SNR was improved by up to 30 dB and the resulting Pearson correlation coefficient was maintained over 99% even for ultrasonic signals with poor initial SNR.