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 …

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

The clarity of medical images is crucial for doctors to identify and diagnose different
diseases. High-resolution images have more detailed information and clearer content than …

Recently, convolutional neural networks (CNNs) have been successfully applied to many
remote sensing problems. However, deep learning techniques for multi-image super …

Due to the importance of security in society, monitoring activities and recognizing specific
people through surveillance video cameras play an important role. One of the main issues in …

It is challenging to restore low-resolution (LR) images to super-resolution (SR) images with
correct and clear details. Existing deep learning works almost neglect the inherent structural …

Example learning-based superresolution (SR) algorithms show promise for restoring a high-
resolution (HR) image from a single low-resolution (LR) input. The most popular …

This paper presents a novel strategy for high-fidelity image restoration by characterizing
both local smoothness and nonlocal self-similarity of natural images in a unified statistical …

Super-resolution (SR) technology is an important way to improve spatial resolution under
the condition of sensor hardware limitations. With the development of deep learning (DL) …

Recent advances have shown how deep neural networks can be extremely effective at
super-resolving remote-sensing imagery, starting from a multitemporal collection of low …