Images are easily polluted by noise in the process of acquisition and transmission, which
will affect people's understanding and utilization of knowledge and information in images …

In this paper, we propose a new deep network architecture named deep boosting denoising
net (DBDnet) for image denoising. It is a residual learning network that can generate a noise …

Recently, deep convolutional neural networks have been applied in numerous image
processing researches and have exhibited drastically improved performances. In this study …

Deep convolutional neural networks (CNNs) for image denoising can effectively exploit rich
hierarchical features and have achieved great success. However, many deep CNN-based …

In order to obtain high-quality images, it is very important to remove noise effectively and
retain image details reasonably. In this paper, we propose a residual UNet denoising …

To achieve promising results on removing noise from real-world images, most of existing
denoising networks are formulated with complex network structure, making them impractical …

In image denoising, deep convolutional neural networks (CNNs) can obtain favorable
performance on removing spatially invariant noise. However, many of these networks cannot …

Existing methods for image denoising mainly focused on noise and visual artifacts too much
but rarely mentioned the loss of edge information. In this paper, we propose a deep …

In recent years, deep convolutional neural network (DCNN) has achieved impressive
performance in image denoising. However, the existing CNN-based methods cannot work …

Image denoising has always been a hot research issue in the computer vision community,
which aims to reduce the noise in digital images to improve image quality. As a key step in …