Hyperspectral image super-resolution meets deep learning: A survey and perspective
Hyperspectral image super-resolution, which refers to reconstructing the high-resolution
hyperspectral image from the input low-resolution observation, aims to improve the spatial …
hyperspectral image from the input low-resolution observation, aims to improve the spatial …
A review of spatial enhancement of hyperspectral remote sensing imaging techniques
N Aburaed, MQ Alkhatib, S Marshall… - IEEE Journal of …, 2023 - ieeexplore.ieee.org
Remote sensing technology has undeniable importance in various industrial applications,
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …
such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding …
ESSAformer: Efficient transformer for hyperspectral image super-resolution
Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-
resolution hyperspectral image from a low-resolution observation. However, the prevailing …
resolution hyperspectral image from a low-resolution observation. However, the prevailing …
Interactformer: Interactive transformer and CNN for hyperspectral image super-resolution
Due to rich spectral information, hyperspectral images (HSIs) have been widely used in
various fields. However, limited by imaging systems, the low spatial resolution of HSIs has …
various fields. However, limited by imaging systems, the low spatial resolution of HSIs has …
Exploring the relationship between 2D/3D convolution for hyperspectral image super-resolution
Hyperspectral image super-resolution (SR) methods based on deep learning have achieved
significant progress recently. However, previous methods lack the joint analysis between …
significant progress recently. However, previous methods lack the joint analysis between …
Deep coupled feedback network for joint exposure fusion and image super-resolution
Nowadays, people are getting used to taking photos to record their daily life, however, the
photos are actually not consistent with the real natural scenes. The two main differences are …
photos are actually not consistent with the real natural scenes. The two main differences are …
An iterative regularization method based on tensor subspace representation for hyperspectral image super-resolution
Hyperspectral image super-resolution (HSI-SR) can be achieved by fusing a paired
multispectral image (MSI) and hyperspectral image (HSI), which is a prevalent strategy. But …
multispectral image (MSI) and hyperspectral image (HSI), which is a prevalent strategy. But …
Dual-stage approach toward hyperspectral image super-resolution
Hyperspectral image produces high spectral resolution at the sacrifice of spatial resolution.
Without reducing the spectral resolution, improving the resolution in the spatial domain is a …
Without reducing the spectral resolution, improving the resolution in the spatial domain is a …
An efficient unfolding network with disentangled spatial-spectral representation for hyperspectral image super-resolution
Hyperspectral image super-resolution (HSI SR) is dramatically impacted by high spectral
dimensionality, insufficient spatial resolution, and limited availability of training samples …
dimensionality, insufficient spatial resolution, and limited availability of training samples …
Hyperspectral image super-resolution via knowledge-driven deep unrolling and transformer embedded convolutional recurrent neural network
Hyperspectral (HS) imaging has been widely used in various real application problems.
However, due to the hardware limitations, the obtained HS images usually have low spatial …
However, due to the hardware limitations, the obtained HS images usually have low spatial …