Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …

Deep learning is becoming an increasingly important tool for image reconstruction in
fluorescence microscopy. We review state-of-the-art applications such as image restoration …

With the goal of recovering high-quality image content from its degraded version, image
restoration enjoys numerous applications, such as in surveillance, computational …

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

In this paper, we introduce NBNet, a novel framework for image denoising. Unlike previous
works, we propose to tackle this challenging problem from a new perspective: noise …

Recent studies have shown that deep networks can achieve promising results for image
denoising. However, how to simultaneously incorporate the valuable achievements of …

Deep convolutional neural networks perform better on images containing spatially invariant
noise (synthetic noise); however, its performance is limited on real-noisy photographs and …

In last few years, supervised deep learning has emerged as one powerful tool for image
denoising, which trains a denoising network over an external dataset of noisy/clean image …

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

The availability of large-scale datasets has helped unleash the true potential of deep
convolutional neural networks (CNNs). However, for the single-image denoising problem …