Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from
the observed LR images. As SR has been developed for more than three decades, both …

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 …

The task of single image super-resolution (SISR) is a highly ill-posed inverse problem since
reconstructing the high-frequency details from a low-resolution image is challenging. Most …

The amount of noise included in a hyperspectral image limits its application and has a
negative impact on hyperspectral image classification, unmixing, target detection, and so on …

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

This paper presents a fast algorithm for restoring video sequences. The proposed algorithm,
as opposed to existing methods, does not consider video restoration as a sequence of …

In many surveillance video applications, it is of interest to recognize a region of interest
(ROI), which often occupies a small portion of a low-resolution, noisy video. This paper …

This paper presents the design and implementation of TurboTrack, a 3D localization system
for fine-grained robotic tasks. TurboTrack's unique capability is that it can localize …

The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution
image based on a set of images, acquired from the same scene and denoted as 'low …

In this paper, we address the super resolution (SR) problem from a set of degraded low
resolution (LR) images to obtain a high resolution (HR) image. Accurate estimation of the …