Recently, single-image super-resolution has made great progress due to the development of
deep convolutional neural networks (CNNs). The vast majority of CNN-based models use a …

Single image super-resolution (SISR) has witnessed great progress as convolutional neural
network (CNN) gets deeper and wider. However, enormous parameters hinder its …

In recent years, numerous lightweight convolution neural networks (CNNs) have made
remarkable progress for single image super-resolution (SISR) and showed great power for …

We have witnessed great success of Single Image Super-Resolution (SISR) with
convolutional neural networks (CNNs) in recent years. However, most existing Super …

Deep Convolutional Neural Networks (CNN) have drawn great attention in image super-
resolution (SR). Recently, visual attention mechanism, which exploits both of the feature …

Single image super-resolution is an ill-posed problem, whose purpose is to acquire a high-
resolution image from its degraded observation. Existing deep learning-based methods are …

Dilated convolutions support expanding receptive field without parameter exploration or
resolution loss, which turn out to be suitable for pixel-level prediction problems. In this paper …

Deep learning based single image super-resolution methods use a large number of training
datasets and have recently achieved great quality progress both quantitatively and …

One non-negligible flaw of the convolutional neural networks (CNNs) based single image
super-resolution (SISR) models is that most of them are not able to restore high-resolution …

Sparse coding has been widely applied to learning-based single image super-resolution
(SR) and has obtained promising performance by jointly learning effective representations …