Ultrasonic image denoising using machine learning in point contact excitation and detection method

A point contact/Coulomb coupling technique is generally used for visualizing the ultrasonic waves in Lead Zirconate Titanate (PZT) ceramics. The point contact and delta pulse excitation produce a broadband frequency spectrum and wide directional wave vector. In ultrasonic, the signal is corrupted with several types of noises such as speckle, Gaussian, Poisson, and salt and pepper noise. Consequently, the resolution and quality of the images are degraded. The reliability of the health assessment of any civil or mechanical structures highly depends on the ultrasonic signals acquired from the sensors. Recently, deep learning (DL) has been implemented for the reduction of noises from the signals and in images. Here, we have implemented deep learning-based convolutional autoencoders for suitable noise modeling and subsequently denoising the ultrasonic images. Two different metrics, PSNR and SSIM are calculated for quantitative analysis of ultrasonic images. PSNR provides higher visual interpretation, whereas the SSIM can be used to measure much finer similarities. Based upon these parameters speckle-noise demonstrated better than other noise models.

Automated summary

A point contact/Coulomb coupling technique is used to visualize ultrasonic waves in PZT ceramics. Different types of noise, including speckle, Gaussian, Poisson, and salt and pepper noise, corrupt the ultrasonic signal and degrade the quality of images. This study implements deep learning-based convolutional autoencoders for noise modeling and denoising of ultrasonic images. Quantitative analysis using PSNR and SSIM metrics shows that speckle noise model performs better than other noise models.