Abstract Recently, super-resolution (SR) techniques based on deep learning have taken
more and more attention, aiming to improve the images and videos resolutions. Most of the …

Image Super-Resolution (SR) is an important class of image processing techniqueso
enhance the resolution of images and videos in computer vision. Recent years have …

A number of super-resolution (SR) algorithms from in terpolation to deep neural networks
(DNN) have emerged to restore or create missing details of the input low-resolution image …

Image super-resolution (SR) is one of the vital image processing methods that improve the
resolution of an image in the field of computer vision. In the last two decades, significant …

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

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 …

Deep convolutional neural networks (CNNs) with strong expressive ability have achieved
impressive performances on single image super-resolution (SISR). However, their excessive …

Image super-resolution, aims to recover a corresponding high-resolution image from a given
low-resolution image. While most state-of-the-art methods only consider using fixed small …

The aim of Image super-resolution (SR) is to recover high-resolution images from low-
resolution ones. By virtue of the great success in numerous computer vision tasks achieved …

Image super-resolution (SR) is an interesting topic in computer vision. However, it remains
challenging to achieve high-resolution image from the corresponding low-resolution version …