Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that
aims to obtain a high-resolution output from one of its low-resolution versions. Recently …

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

Lossy image compression is generally formulated as a joint rate-distortion optimization
problem to learn encoder, quantizer, and decoder. Due to the non-differentiable quantizer …

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 …

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 …

Mixed pixel problem is omnipresent in remote sensing images for urban land use
interpretation due to the hardware limitations. Subpixel mapping (SPM) is a usual way to …

Image super-resolution aims to increase the resolution of images with good visual
experience. Over the past decades, there have been many image super-resolution …

Sparse representation-based image super-resolution is a well-studied topic; however, a
general sparse framework that can utilize both internal and external dependencies remains …

Super-resolution (SR) is by definition ill-posed. There are infinitely many plausible high-
resolution variants for a given low-resolution natural image. Most of the current literature …

In this paper, we propose a new video Super-Resolution (SR) method by jointly modeling
intra-frame redundancy and inter-frame motion context in a unified deep network. Different …