Deep models trained in supervised mode have achieved remarkable success on a variety of
tasks. When labeled samples are limited, self-supervised learning (SSL) is emerging as a …

Deep learning is becoming an increasingly important tool for image reconstruction in
fluorescence microscopy. We review state-of-the-art applications such as image restoration …

Abstract Neural Radiance Fields (NeRF) is a technique for high quality novel view synthesis
from a collection of posed input images. Like most view synthesis methods, NeRF uses …

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 …

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 …

Self-and semi-supervised learning frameworks have made significant progress in training
machine learning models with limited labeled data in image and language domains. These …

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 …

In last few years, supervised deep learning has emerged as one powerful tool for image
denoising, which trains a denoising network over an external dataset of noisy/clean image …