Ntire 2020 challenge on real-world image super-resolution: Methods and results
A Lugmayr, M Danelljan… - Proceedings of the IEEE …, 2020 - openaccess.thecvf.com
This paper reviews the NTIRE 2020 challenge on real world super-resolution. It focuses on
the participating methods and final results. The challenge addresses the real world setting …
the participating methods and final results. The challenge addresses the real world setting …
Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Neighbor2neighbor: Self-supervised denoising from single noisy images
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 …
neural networks. However, the requirement of large amounts of noisy-clean image pairs for …
Noise2void-learning denoising from single noisy images
The field of image denoising is currently dominated by discriminative deep learning methods
that are trained on pairs of noisy input and clean target images. Recently it has been shown …
that are trained on pairs of noisy input and clean target images. Recently it has been shown …
Recorrupted-to-recorrupted: Unsupervised deep learning for image denoising
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 …
development on image denoising. In the last few years, there is an increasing interest in …
Nbnet: Noise basis learning for image denoising with subspace projection
In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous
works, we propose to tackle this challenging problem from a new perspective: noise …
works, we propose to tackle this challenging problem from a new perspective: noise …
Adaptive consistency prior based deep network for image denoising
C Ren, X He, C Wang, Z Zhao - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Recent studies have shown that deep networks can achieve promising results for image
denoising. However, how to simultaneously incorporate the valuable achievements of …
denoising. However, how to simultaneously incorporate the valuable achievements of …
Self2self with dropout: Learning self-supervised denoising from single image
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 …
denoising, which trains a denoising network over an external dataset of noisy/clean image …
Masked image training for generalizable deep image denoising
When capturing and storing images, devices inevitably introduce noise. Reducing this noise
is a critical task called image denoising. Deep learning has become the de facto method for …
is a critical task called image denoising. Deep learning has become the de facto method for …
Variational denoising network: Toward blind noise modeling and removal
Blind image denoising is an important yet very challenging problem in computer vision due
to the complicated acquisition process of real images. In this work we propose a new …
to the complicated acquisition process of real images. In this work we propose a new …