Web: http://arxiv.org/abs/2101.09122

May 5, 2022, 1:10 a.m. | Simone Cammarasana, Paolo Nicolardi, Giuseppe Patanè

cs.CV updates on arXiv.org arxiv.org

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 …

arxiv deep deep learning deep learning framework denoising framework images learning real-time time

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