The advent of deep learning has brought a revolutionary transformation to image denoising
techniques. However, the persistent challenge of acquiring noise-clean pairs for supervised …

Recent advances in deep learning have been pushing image denoising techniques to a
new level. In self-supervised image denoising, blind-spot network (BSN) is one of the most …

In recent years, the development of deep learning has been pushing image denoising to a
new level. Among them, self-supervised denoising is increasingly popular because it does …

Significant progress has been made in self-supervised image denoising (SSID) in the recent
few years. However, most methods focus on dealing with spatially independent noise, and …

Despite the significant results on synthetic noise under simplified assumptions, most self-
supervised denoising methods fail under real noise due to the strong spatial noise …

With sufficient paired training samples, the supervised deep learning methods have
attracted much attention in image denoising because of their superior performance …

Deep learning methods have shown remarkable performance in image denoising,
particularly when trained on large-scale paired datasets. However, acquiring such paired …

With its significant performance improvements, the deep learning paradigm has become a
standard tool for modern image denoisers. While promising performance has been shown …

Real-world single image denoising is crucial and practical in computer vision. Bayesian
inversions combined with score priors now have proven effective for single image denoising …

Deep learning (DL)-based denoising of low-dose positron emission tomography (LDPET)
and low-dose computed tomography (LDCT) has been widely explored. However, previous …