Abstract Recently, super-resolution (SR) techniques based on deep learning have taken
more and more attention, aiming to improve the images and videos resolutions. Most of the …

Hyperspectral image super-resolution (HSI SR) is dramatically impacted by high spectral
dimensionality, insufficient spatial resolution, and limited availability of training samples …

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 …

Hyperspectral intrinsic image decomposition (HyperIID) has been proven to be a very useful
approach to reduce the spectral uncertainty in the remote sensing imaging process and …

The single image super-resolution (SISR) task has received much attention due to the wide
range of applications in many tasks. The progress in this SISR is mainly based on deep …

Recent advances in deep learning have transformed data-driven geoscientific analysis. In
particular, the adoption of attention mechanism in deep learning has received considerable …

A hyperspectral image (HSI) has a very high spectral resolution, which can reflect the
target's material properties well. However, the limited spatial resolution poses a constraint …

Due to the inadequacy in utilizing complementary information from different modalities and
the biased estimation of degraded parameters, the unsupervised hyperspectral super …

The hyperspectral image (HSI) distinguishes itself in material identification through its
exceptional spectral resolution. However, its spatial resolution is constrained by hardware …

Spectral super-resolution (SSR) aims to improve the spectral resolution of images from
multispectral imagery or even red, green, blue (RGB) images. However, the majority of …