Pervasive growth and usage of the Internet and mobile applications have expanded
cyberspace. The cyberspace has become more vulnerable to automated and prolonged …

Removal of noise from an image is an extensively studied problem in image processing.
Indeed, the recent advent of sophisticated and highly effective denoising algorithms has led …

The recent phenomenal interest in convolutional neural networks (CNNs) must have made it
inevitable for the super-resolution (SR) community to explore its potential. The response has …

Many material and biological samples in scientific imaging are characterized by nonlocal
repeating structures. These are studied using scanning electron microscopy and electron …

Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing
systems and deep learning-based methods have achieved remarkable success. However …

We address single image super-resolution using a statistical prediction model based on
sparse representations of low-and high-resolution image patches. The suggested model …

Hyperspectral image (HSI) denoising is an essential preprocess step to improve the
performance of subsequent applications. For HSI, there is much global and local …

This paper presents a novel single-image super-resolution (SR) procedure, which upscales
a given low-resolution (LR) input image to a high-resolution image while preserving the …

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by
enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of …

Convolutional neural networks (CNNs) have been widely used in image super-resolution
(SR). Most existing CNN-based methods focus on achieving better performance by …