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 …

This paper reviews the first challenge on single image super-resolution (restoration of rich
details in an low resolution image) with focus on proposed solutions and results. A new …

Abstract Multi-scale Vision Transformer (ViT) has emerged as a powerful backbone for
computer vision tasks, while the self-attention computation in Transformer scales …

Due to strong learning ability, convolutional neural networks (CNNs) have been developed
in image denoising. However, convolutional operations may change original distributions of …

Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …

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

The tradeoff between receptive field size and efficiency is a crucial issue in low level vision.
Plain convolutional networks (CNNs) generally enlarge the receptive field at the expense of …

Although recent studies empirically show that injecting Convolutional Neural Networks
(CNNs) into Vision Transformers (ViTs) can improve the performance of person re …

This paper introduces a novel large dataset for example-based single image super-
resolution and studies the state-of-the-art as emerged from the NTIRE 2017 challenge. The …

Image restoration algorithms are typically evaluated by some distortion measure (eg PSNR,
SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this …