The task of single image super resolution (SISR) has taken much attention in the last few
years due to the wide range of real-world applications. However, most of the recently …

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 …

Recent advances in single image super-resolution (SISR) have achieved extraordinary
performance, but the computational cost is too heavy to apply in edge devices. To alleviate …

Learning continuous image representations is recently gaining popularity for image super-
resolution (SR) because of its ability to reconstruct high-resolution images with arbitrary …

Recently, very deep convolutional neural networks (CNNs) have shown great power in
single image super-resolution (SISR) and achieved significant improvements against …

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 …

Deep learning based approaches has achieved great performance in single image super-
resolution (SISR). However, recent advances in efficient super-resolution focus on reducing …

Recently, several lightweight methods have been proposed to implement single-image
super-resolution (SISR) on resource-constrained devices. However, these methods primarily …

Recent breakthroughs in single image super resolution have investigated the potential of
deep Convolutional Neural Networks (CNNs) to improve performance. However, CNNs …

Single image super-resolution (SISR) has witnessed great strides with the development of
deep learning. However, most existing studies focus on building more complex networks …