Single image super resolution (SISR) is to reconstruct a high resolution image from a single
low resolution image. The SISR task has been a very attractive research topic over the last …

Single image super resolution aims to enhance image quality with respect to spatial content,
which is a fundamental task in computer vision. In this work, we address the task of single …

Recovering texture information from the aliasing regions has always been a major challenge
for Single Image Super Resolution (SISR) task. These regions are often submerged in noise …

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

This paper proposes a DenseNet with deep Residual Channel Attention (DRCA) for single
image super resolution. Recent works have shown that skip connections between layers …

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

The use of deep convolutional neural networks (CNNs) for image super-resolution (SR) from
low-resolution (LR) input has achieved remarkable reconstruction performance with the …

Single image super-resolution (SR) is an ill-posed problem which aims to recover high-
resolution (HR) images from their low-resolution (LR) observations. The crux of this problem …

Recently, deep convolutional neural network (CNN) have achieved promising performance
for single image super-resolution (SISR). However, they usually extract features on a single …

Recently, it has been demonstrated that deep neural networks can significantly improve the
performance of single image super-resolution (SISR). Numerous studies have concentrated …