Image denoising is of vital importance in many imaging or computer vision related areas.
With the convolutional neural networks showing strong capability in computer vision tasks …

Following the performance breakthrough of denoising networks, improvements have come
chiefly through novel architecture designs and increased depth. While novel denoising …

In image denoising, the recovery of high-frequency regions, such as image edges, directly
affects the quality of the denoised images. However, previous deep learning-based …

Despite the tremendous advances in denoising techniques, it's still challenging to restore a
clean image with salient structures based on one noisy observation, especially at high noise …

Convolutional neural network (CNN)-based image denoising methods have been widely
studied recently, because of their high-speed processing capability and good visual quality …

High-frequency regions like edges compromise the image denoising performance. In
traditional hand-crafted systems, image edges/textures were regularly used to restore the …

Convolutional neural networks (CNNs) have shown outstanding performance on image
denoising with the help of large-scale datasets. Earlier methods naively trained a single …

Motivated by the advance of deep learning methods, deep unfolding methods such as deep
convolutional dictionary learning have achieved great success in image denoising tasks …

In this work, we explore an innovative strategy for image denoising by using convolutional
neural networks (CNN) to learn pixel-distribution from noisy data. By increasing CNN's width …

Recently, deep learning techniques are soaring and have shown dramatic improvements in
real-world noisy image denoising. However, the statistics of real noise generally vary with …