Convolutional neural networks (CNNs) have performed well in image denoising. Although
some CNNs enlarge convolutional kernels and increase stacked convolutional layers to …

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 …

Deep convolutional neural networks (CNNs) for image denoising have recently attracted
increasing research interest. However, plain networks cannot recover fine details for a …

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

Previous works have shown that convolutional neural networks can achieve good
performance in image denoising tasks. However, limited by the local rigid convolutional …

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

Image denoising methods using deep neural networks have achieved a great progress in
the image restoration. However, the recovered images restored by these deep denoising …

Convolutional neural networks (CNNs) can effectively extract structural information in image
denoising. However, they tend to ignore texture information. To tackle this problem, we …

Due to the favorable denoising performance of the discriminative learning model, a large
number of discriminative learning models have been designed to remove noise. However …

Deep convolutional neural networks have achieved great performance on various image
restoration tasks. Specifically, the residual dense network (RDN) has achieved great results …