In this work, we present QuickSRNet, an efficient super-resolution architecture for real-time
applications on mobile platforms. Super-resolution clarifies, sharpens, and upscales an …

Though recent years have witnessed remarkable progress in single image super-resolution
(SISR) tasks with the prosperous development of deep neural networks (DNNs), the deep …

Single-image super-resolution technology has become a topic of extensive research in
various applications, aiming to enhance the quality and resolution of degraded images …

Super-resolution (SR) has achieved great success due to the development of deep
convolutional neural networks (CNNs). However, as the depth and width of the networks …

Lightweight image super-resolution (SR) networks have the utmost significance for real-
world applications. There are several deep learning based SR methods with remarkable …

Current CNN-based super-resolution (SR) methods process all locations equally with
computational resources being uniformly assigned in space. However, since missing details …

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

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

We propose a real-time and lightweight single-image super-resolution (SR) network named
Bicubic++. Despite using spatial dimensions of the input image across the whole network …

Classic image scaling (eg bicubic) can be seen as one convolutional layer and a single
upscaling filter. Its implementation is ubiquitous in all display devices and image processing …