A Complementary Global and Local Knowledge Network for Ultrasound
  denoising with Fine-grained Refinement
Zhenyu Bu Kai-Ni Wang Fuxing Zhao Shengxiao Li Guang-Quan Zhou
Ultrasound imaging serves as an effective and non-invasive diagnostic tool
commonly employed in clinical examinations. However, the presence of speckle
noise in ultrasound images invariably degrades image quality, impeding the
performance of subsequent tasks, such as segmentation and classification.
Existing methods for speckle noise reduction frequently induce excessive image
smoothing or fail to preserve detailed information adequately. In this paper,
we propose a complementary global and local knowledge network for ultrasound
denoising with fine-grained refinement. Initially, the proposed architecture
employs the L-CSwinTransformer as encoder to capture global information,
incorporating CNN as decoder to fuse local features. We expand the resolution
of the feature at different stages to extract more global information compared
to the original CSwinTransformer. Subsequently, we integrate Fine-grained
Refinement Block (FRB) within the skip-connection stage to further augment
features. We validate our model on two public datasets, HC18 and BUSI.
Experimental results demonstrate that our model can achieve competitive
performance in both quantitative metrics and visual performance. Our code will
be available at https://github.com/AAlkaid/USDenoising.