Super resolution (SR) is an eminent system in the field of computer vison and image
processing to improve the visual perception of the poor-quality images. The key objective of …

Super-resolution, the process of obtaining one or more high-resolution images from one or
more low-resolution observations, has been a very attractive research topic over the last two …

Despite that convolutional neural networks (CNN) have recently demonstrated high-quality
reconstruction for single-image super-resolution (SR), recovering natural and realistic …

Self-similarity based super-resolution (SR) algorithms are able to produce visually pleasing
results without extensive training on external databases. Such algorithms exploit the …

We propose a deep learning method for single image super-resolution (SR). Our method
directly learns an end-to-end map** between the low/high-resolution images. The …

We address the problem of image upscaling in the form of single image super-resolution
based on a dictionary of low-and high-resolution exemplars. Two recently proposed …

In this paper we present seven techniques that everybody should know to improve example-
based single image super resolution (SR): 1) augmentation of data, 2) use of large …

Single-image super-resolution is of great importance for vision applications, and numerous
algorithms have been proposed in recent years. Despite the demonstrated success, these …

By benefiting from perceptual losses, recent studies have improved significantly the
performance of the super-resolution task, where a high-resolution image is resolved from its …

The goal of single-image super-resolution is to generate a high-quality high-resolution
image based on a given low-resolution input. It is an ill-posed problem which requires …