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 …

In the last few years, image denoising has benefited a lot from the fast development of
neural networks. However, the requirement of large amounts of noisy-clean image pairs for …

Real noisy-clean pairs on a large scale are costly and difficult to obtain. Meanwhile,
supervised denoisers trained on synthetic data perform poorly in practice. Self-supervised …

PET is a noninvasive molecular imaging modality that is increasingly popular in oncology,
neurology, cardiology, and other fields. 1–3 Accurate quantitation of PET radiotracer uptake …

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 …

Deep denoiser, the deep network for denoising, has been the focus of the recent
development on image denoising. In the last few years, there is an increasing interest in …

Blind-spot network (BSN) and its variants have made significant advances in self-supervised
denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less practical …

Cryogenic electron tomography (cryoET) allows visualization of cellular structures in situ.
However, anisotropic resolution arising from the intrinsic “missing-wedge” problem has …

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 …