  Ultrasound images are widespread in medical diagnosis for muscle-skeletal,
cardiac, and obstetrical diseases, due to the efficiency and non-invasiveness
of the acquisition methodology. However, ultrasound acquisition introduces a
speckle noise in the signal, that corrupts the resulting image and affects
further processing operations, and the visual analysis that medical experts
conduct to estimate patient diseases. Our main goal is to define a universal
deep learning framework for real-time denoising of ultrasound images. We
analyse and compare state-of-the-art methods for the smoothing of ultrasound
images (e.g., spectral, low-rank, and deep learning denoising algorithms), in
order to select the best one in terms of accuracy, preservation of anatomical
features, and computational cost. Then, we propose a tuned version of the
selected state-of-the-art denoising methods (e.g., WNNM), to improve the
quality of the denoised images, and extend its applicability to ultrasound
images. To handle large data sets of ultrasound images with respect to
applications and industrial requirements, we introduce a denoising framework
that exploits deep learning and HPC tools, and allows us to replicate the
results of state-of-the-art denoising methods in a real-time execution.
