Deep neural networks (DNNs) have witnessed remarkable achievement in image super-
resolution (SR), and plenty of DNN-based SR models with elaborated network designs have …

We aim at accelerating super-resolution (SR) networks on large images (2K-8K). The large
images are usually decomposed into small sub-images in practical usages. Based on this …

In recent years, constructing various deep convolutional neural networks (CNNs) for single-
image super-resolution (SISR) tasks has made significant progress. Despite their high …

Convolutional neural networks (CNNs) have recently been widely and successfully applied
in the image computer vision community, and obtained great advances in single image …

Single image super-resolution (SR) has been boosted by deep convolutional neural
networks with growing model complexity and computational costs. To deploy existing SR …

The rapid development of deep learning (DL) has driven single image super-resolution (SR)
into a new era. However, in most existing DL based image SR networks, the information …

Convolutional neural networks have been proven to be of great benefit for single-image
super-resolution (SISR). However, previous works do not make full use of multi-scale …

Recently, a number of CNN based methods have made great progress in single image
super-resolution. However, these existing architectures commonly build massive number of …

Deep convolutional neural network (CNN) approaches have achieved impressive
performance for image super-resolution (SR). The main issue of image SR is to effectively …

In order to solve the problem of deploying super-resolution technology on resource-limited
devices, this paper explores the differences in performance and efficiency between …