Image super-resolution is one of the most popular computer vision problems with many
important applications to mobile devices. While many solutions have been proposed for this …

Image super-resolution is a common task on mobile and IoT devices, where one often needs
to upscale and enhance low-resolution images and video frames. While numerous solutions …

Abstract Single-Image Super Resolution (SISR) is a classical computer vision problem and it
has been studied for over decades. With the recent success of deep learning methods …

Along with the rapid development of real-world applications, higher requirements on the
accuracy and efficiency of image super-resolution (SR) are brought forward. Though existing …

This paper studies the single image super-resolution problem using adder neural networks
(AdderNets). Compared with convolutional neural networks, AdderNets utilize additions to …

Abstract Model quantization is a crucial step for deploying super resolution (SR) networks on
mobile devices. However, existing works focus on quantization-aware training, which …

Efficient deep learning-based approaches have achieved remarkable performance in single
image super-resolution. However, recent studies on efficient super-resolution have mainly …

Deep convolutional neural networks (DCNNs) have shown dominant performance in the
task of super-resolution (SR). However, their heavy memory cost and computation overhead …

Despite breakthrough advances in image super-resolution (SR) with convolutional neural
networks (CNNs), SR has yet to enjoy ubiquitous applications due to the high computational …

With the rising popularity of intelligent mobile devices, it is of great practical significance to
develop accurate, real-time and energy-efficient image Super-Resolution (SR) methods. A …