This review article provides a comprehensive survey on state-of-the-art impulse and
Gaussian denoising filters applied to images and summarizes the progress that has been …

This paper reviews the NTIRE 2020 challenge on real image denoising with focus on the
newly introduced dataset, the proposed methods and their results. The challenge is a new …

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 …

While deep convolutional neural networks (CNNs) have achieved impressive success in
image denoising with additive white Gaussian noise (AWGN), their performance remains …

Due to the fast inference and good performance, discriminative learning methods have been
widely studied in image denoising. However, these methods mostly learn a specific model …

The last decade has seen an astronomical shift from imaging with DSLR and point-and-
shoot cameras to imaging with smartphone cameras. Due to the small aperture and sensor …

In this paper, we consider a typical image blind denoising problem, which is to remove
unknown noise from noisy images. As we all know, discriminative learning based methods …

Abstract Machine learning techniques work best when the data used for training resembles
the data used for evaluation. This holds true for learned single-image denoising algorithms …

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 …

Most of existing image denoising methods assume the corrupted noise to be additive white
Gaussian noise (AWGN). However, the realistic noise in real-world noisy images is much …