Convolutional neural network (CNN) depth is of crucial importance for image super-
resolution (SR). However, we observe that deeper networks for image SR are more difficult …

This work aims at designing a lightweight convolutional neural network for image super
resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective …

In this paper, we propose dense feature fusion (DFF) for image super-resolution (SR). As the
same content in different natural images often have various scales and angles of view …

Deep learning based image Super-Resolution (SR) has shown rapid development due to its
ability of big data digestion. Generally, deeper and wider networks can extract richer feature …

Abstract Deep Convolutional Neural Networks (CNNs) have achieved remarkable results on
Single Image Super-Resolution (SISR). Despite considering only a single degradation …

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

Recent studies have shown that deep neural networks can significantly improve the quality
of single-image super-resolution. Current researches tend to use deeper convolutional …

Deep learning techniques have been successfully applied in many areas of computer vision,
including low-level image restoration problems. For image super-resolution, several models …

Recent advances in single image super-resolution (SISR) explore the power of deep
convolutional neural networks (CNNs) to achieve better performance. However, most of the …

In recent years, deep learning methods have been successfully applied to single-image
super-resolution tasks. Despite their great performances, deep learning methods cannot be …