  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
noise in the signal, which corrupts the resulting image and affects further
processing steps, e.g., segmentation and quantitative analysis. We define a
novel deep learning framework for the real-time denoising of ultrasound images.
Firstly, we compare state-of-the-art methods for denoising (e.g., spectral,
low-rank methods) and select WNNM (Weighted Nuclear Norm Minimisation) as the
best denoising in terms of accuracy, preservation of anatomical features, and
edge enhancement. Then, we propose a tuned version of WNNM (tuned-WNNM) that
improves the quality of the denoised images and extends its applicability to
ultrasound images. Through a deep learning framework, the tuned-WNNM
qualitatively and quantitatively replicates WNNM results in real-time. Finally,
our approach is general in terms of its building blocks and parameters of the
deep learning and high-performance computing framework; in fact, we can select
different denoising algorithms and deep learning architectures.
