Lacking realistic ground truth data, image denoising techniques are traditionally evaluated
on images corrupted by synthesized iid Gaussian noise. We aim to obviate this unrealistic …

We design a novel network architecture for learning discriminative image models that are
employed to efficiently tackle the problem of grayscale and color image denoising. Based on …

High-dimensional features are extensively accessible in machine learning and computer
vision areas. How to learn an efficient feature representation for specific learning tasks is …

Blind and universal image denoising consists of using a unique model that denoises images
with any level of noise. It is especially practical as noise levels do not need to be known …

With the emergence of powerful deep learning tools, more and more effective deep
denoisers have advanced the field of image denoising. However, the huge progress made …

Image denoising algorithms are evaluated using images corrupted by artificial noise, which
may lead to incorrect conclusions about their performances on real noise. In this paper we …

We consider the challenging blind denoising problem for Poisson-Gaussian noise, in which
no additional information about clean images or noise level parameters is available …

We propose a fully convolutional neural-network architecture for image denoising which is
simple yet powerful. Its structure allows to exploit the gradual nature of the denoising …

Enhancing low light videos, which consists of denoising and brightness adjustment, is an
intriguing but knotty problem. Under low light condition, due to high sensitivity camera …

Image denoising has achieved unprecedented progress as great efforts have been made to
exploit effective deep denoisers. To improve the denoising performance in real-world, two …