Deep convolutional neural networks, particularly large models with large kernels (3× 3 or
more), have achieved significant progress in single image super-resolution (SISR) tasks …

Deep learning-based single image super-resolution (SISR) approaches have drawn much
attention and achieved remarkable success on modern advanced GPUs. However, most …

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

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 …

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 …

Image super-resolution (SR) is a fast-moving field with novel architectures attracting the
spotlight. However, most SR models were optimized with dated training strategies. In this …

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 …

Convolutional neural network (CNN) based methods have recently achieved extraordinary
performance in single image super-resolution (SISR) tasks. However, most existing CNN …

Single image super-resolution (SR) is extremely difficult if the upscaling factors of image
pairs are unknown and different from each other, which is common in real image SR. To …

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