Abstract
‘Speckle’ refers to the granular patterns that occur in ultrasound images due to wave interference. Speckle removal
can greatly improve the visibility of the underlying structures in an ultrasound image and enhance subsequent postprocessing. We present a novel framework for speckle removal based on low-rank non-local filtering. Our approach
works by first computing a guidance image that assists in
the selection of candidate patches for non-local filtering
in the face of significant speckles. The candidate patches are further refined using a low-rank minimization estimated using a truncated weighted nuclear norm (TWNN)
and structured sparsity. We show that the proposed filtering
framework produces results that outperform state-of-the-art
methods both qualitatively and quantitatively. This framework also provides better segmentation results when used
for pre-processing ultrasound images.