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 …

Recent advances in image super-resolution (SR) explored the power of deep learning to
achieve a better reconstruction performance. However, the feedback mechanism, which …

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 …

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 networks have achieved great success in the field of image super-
resolution. However, many networks do not fully combine high-level and low-level …

Convolutional neural networks have allowed remarkable advances in single image super-
resolution (SISR) over the last decade. Among recent advances in SISR, attention …

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 networks (CNNs) have obtained remarkable performance via deep
architectures. However, these CNNs often achieve poor robustness for image super …

In existing deep network-based image super-resolution (SR) methods, each network is only
trained for a fixed upscaling factor and can hardly generalize to unseen factors at test time …

Deep convolutional neural networks (CNNs) have boosted the performance of image super-
resolution (SR) in recent years. However, existing deep CNN-based SR approaches often …