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 …

In this paper, we present new data pre-processing and augmentation techniques for DNN-
based raw image denoising. Compared with traditional RGB image denoising, performing …

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

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

Networks using down-scaling and up-scaling of feature maps have been studied extensively
in low-level vision research owing to efficient GPU memory usage and their capacity to yield …

During the past years, tremendous advances in image restoration tasks have been achieved
using highly complex neural networks. Despite their good restoration performance, the …

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

Image denoising is a classical topic yet still a challenging problem, especially for reducing
noise from the texture information. Feature scaling (eg, downscale and upscale) is a widely …

Conventionally, image denoising and high-level vision tasks are handled separately in
computer vision. In this paper, we cope with the two jointly and explore the mutual influence …

Recent advances in deep learning have been pushing image denoising techniques to a
new level. In self-supervised image denoising, blind-spot network (BSN) is one of the most …