Image denoising is a crucial step in medical image analysis, as it can significantly enhance
the visual quality of noisy image samples and accelerate the diagnostic process. In the case …

Recently, denoising methods based on supervised learning have exhibited promising
performance. However, their reliance on external datasets containing noisy-clean image …

Background Ultrasound imaging has been recognized as a powerful tool in clinical
diagnosis. Nonetheless, the presence of speckle noise degrades the signal-to-noise of …

Medical imaging denoising faces great challenges, yet is in great demand. With its
distinctive characteristics, medical imaging denoising in the image domain requires …

With the advent of advances in self-supervised learning, paired clean-noisy data are no
longer required in deep learning-based image denoising. However, existing blind denoising …

Image denoising based on deep learning has made more extensive development by using a
large amount of data for network training, however, it is difficult to obtain clean images …

Deep learning-based image denoising plays a critical role in medical imaging, especially
when dealing with rapid fluorescence and ultrasound captures where traditional noise …

Despite being challenging, research on single image based denoising is enjoying a recent
upsurge due to the impressive and dominating performance of deep networks. Most of the …

In this work, we present Eformer-Edge enhancement based transformer, a novel architecture
that builds an encoder-decoder network using transformer blocks for medical image …

Existing methods for image denoising mainly focused on noise and visual artifacts too much
but rarely mentioned the loss of edge information. In this paper, we propose a deep …