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

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 …

Recent advances in single image super-resolution (SISR) have achieved extraordinary
performance, but the computational cost is too heavy to apply in edge devices. To alleviate …

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 …

Recent advances in single image super-resolution (SISR) explored the power of
convolutional neural network (CNN) to achieve a better performance. Despite the great …

Recent research on super-resolution has progressed with the development of deep
convolutional neural networks (DCNN). In particular, residual learning techniques exhibit …

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

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 …

Recently, very deep convolutional neural networks (CNNs) have shown great power in
single image super-resolution (SISR) and achieved significant improvements against …

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