Image denoising, particularly Gaussian denoising, has achieved continuous success in the
past decades. Although deep convolutional neural networks (CNNs) are also shown leading …

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

Deep convolutional neural networks and generative adversarial networks currently attracted
the attention of researchers because it is more effective than conventional representation …

Image denoising is an essential part of many image processing and computer vision tasks
due to inevitable noise corruption during image acquisition. Traditionally, many researchers …

In recent years, deep convolutional neural networks have shown fascinating performance in
the field of image denoising. However, deeper network architectures are often accompanied …

Image denoising is the process of removing noise from noisy images, which is an image
domain transferring task, ie, from a single or several noise level domains to a photo-realistic …

Existing discriminative learning methods for image denoising use either a single residual
learning or a nonresidual learning design. However, we observe that these two schemes …

Noise removal of images is an essential preprocessing procedure for many computer vision
tasks. Currently, many denoising models based on deep neural networks can perform well …

Recent studies on learning-based image denoising have achieved promising performance
on various noise reduction tasks. Most of these deep denoisers are trained either under the …

Fast and flexible processing are two essential requirements for a number of practical
applications of image denoising. Current state-of-the-art methods, however, still require …