Hyperspectral image super-resolution, which refers to reconstructing the high-resolution
hyperspectral image from the input low-resolution observation, aims to improve the spatial …

Abstract Single Image Super Resolution (SISR) is a process to obtain a high pixel density
and refined details from a low resolution (LR) image to get upscaled and sharper high …

Despite the proven significance of hyperspectral images (HSIs) in performing various
computer vision tasks, its potential is adversely affected by the low-resolution (LR) property …

Remote sensing technology has undeniable importance in various industrial applications,
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …

In recent years, there has been a growing emphasis on assessing and ensuring the quality
of horticultural and agricultural produce. Traditional methods involving field measurements …

Hyperspectral image (HSI) synthesis overcomes the limitations of imaging sensors and
enables low-cost acquisition of HSIs with high spatial resolution. Using RGB as a conditional …

With recent advances in artificial intelligence (AI) and robotics, unmanned vehicle swarms
have received great attention from both academia and industry due to their potential to …

The neural radiance field (NeRF) achieved remarkable success in modeling 3D scenes and
synthesizing high-fidelity novel views. However, existing NeRF-based methods focus more …

Single hyperspectral image (HSI) super-resolution (SR) is an important topic in the remote-
sensing field. However, existing HSI SR methods mainly use the feed-forward upsampling …

The aim of fusing hyperspectral and multispectral images is to overcome the limitation of
remote sensing hyperspectral sensors by improving their spatial resolutions. This process …