With the achievements of deep learning, applications of deep convolutional neural networks
for the image denoising problem have been widely studied. However, these methods are …

Owing to recent advancements, very deep convolutional neural networks (CNNs) have
found application in image denoising. However, while deeper models lead to better …

Deep convolutional neural networks (CNNs) have attracted considerable interest in low-
level computer vision. Researches are usually devoted to improving the performance via …

Due to the rapid advancement of GPU computing, deep learning has lately been widely
used in image denoising. Most deep learning methods require noise-free images as labels …

Deep convolutional neural networks (CNNs) have attracted great attention in the field of
image denoising. However, there are two drawbacks:(1) it is very difficult to train a deeper …

Recently, convolutional neural network (CNN)-based methods have achieved impressive
performance on image denoising. Notably, CNN with deeper and thinner structures is more …

Due to the fast inference and good performance, convolutional neural network (CNN) has
been widely applied in image denoising. Some new approaches, such as residual learning …

Deep convolutional neural networks (DCNN) have attracted considerable interest in image
denoising because of their excellent learning capacity. However, most of the existing …

Deep convolutional neural networks (CNNs) with strong learning abilities have been used in
the field of image denoising. However, some CNNs depend on a single deep network to …

Affected by the hardware conditions and environment of imaging, images generally have
serious noise. The presence of noise diminishes the image quality and compromises its …