This paper reviews the NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement
of Compressed Video. In this challenge, we proposed the LDV 2.0 dataset, which …

Recently developed deep-learning-based denoisers often outperform state-of-the-art
conventional denoisers, such as the BM3D. They are typically trained to minimizethe mean …

Recently, Stein's unbiased risk estimator (SURE) has been applied to unsupervised training
of deep neural network Gaussian denoisers that outperformed classical non-deep learning …

Compressive sensing is a method to recover the original image from undersampled
measurements. In order to overcome the ill-posedness of this inverse problem, image priors are …

Recently, deep neural network (DNN) based methods for low-dose CT have been
investigated to achieve excellent performance in both image quality and computational speed. …

Recently, there have been several works on unsupervised learning for training deep
learning based denoisers without clean images. Approaches based on Stein's unbiased risk …

Image denoising is a one of the most important tasks in computer vision that can improve
the performance of more higher level tasks such as image classification, segmentation and …

Recently, deep learning based compressive recovery methods have been proposed and
have yielded state-of-the-art performances. Ironically, training deep neural networks for them …

We tackle a challenging blind image denoising problem, in which only single distinct noisy
images are available for training a denoiser, and no information about noise is known, except …

This paper reviews the NTIRE 2019 challenge on real image denoising with focus on the
proposed methods and their results. The challenge has two tracks for quantitatively evaluating …