A survey on hyperspectral image restoration: From the view of low-rank tensor approximation
The ability to capture fine spectral discriminative information enables hyperspectral images
(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the …
(HSIs) to observe, detect and identify objects with subtle spectral discrepancy. However, the …
Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution
Due to the limitations of hyperspectral imaging systems, hyperspectral imagery (HSI) often
suffers from poor spatial resolution, thus hampering many applications of the imagery …
suffers from poor spatial resolution, thus hampering many applications of the imagery …
Learning spatial-spectral prior for super-resolution of hyperspectral imagery
Recently, single gray/RGB image super-resolution reconstruction task has been extensively
studied and made significant progress by leveraging the advanced machine learning …
studied and made significant progress by leveraging the advanced machine learning …
MLR-DBPFN: A multi-scale low rank deep back projection fusion network for anti-noise hyperspectral and multispectral image fusion
W Sun, K Ren, X Meng, G Yang, C Xiao… - IEEE transactions on …, 2022 - ieeexplore.ieee.org
Fusing low spatial resolution (LR) hyperspectral (HS) data and high spatial resolution (HR)
multispectral (MS) data aims to obtain HR HS data. However, due to bad weather and the …
multispectral (MS) data aims to obtain HR HS data. However, due to bad weather and the …
Local semantic feature aggregation-based transformer for hyperspectral image classification
B Tu, X Liao, Q Li, Y Peng… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Hyperspectral images (HSIs) contain abundant information in the spatial and spectral
domains, allowing for a precise characterization of categories of materials. Convolutional …
domains, allowing for a precise characterization of categories of materials. Convolutional …
Iterative deep homography estimation
Abstract We propose Iterative Homography Network, namely IHN, a new deep homography
estimation architecture. Different from previous works that achieve iterative refinement by …
estimation architecture. Different from previous works that achieve iterative refinement by …
NonRegSRNet: A nonrigid registration hyperspectral super-resolution network
Due to the limitations of imaging systems, satellite hyperspectral imagery (HSI), which yields
rich spectral information in many channels, often suffers from poor spatial resolution. HSI …
rich spectral information in many channels, often suffers from poor spatial resolution. HSI …
A survey of hyperspectral image super-resolution technology
M Zhang, X Sun, Q Zhu, G Zheng - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Hyperspectral images (HSIs) have very high spectral resolution, which can reflect the
characteristics of different materials well. However, compared with RGB image or …
characteristics of different materials well. However, compared with RGB image or …
Diffused convolutional neural network for hyperspectral image super-resolution
With the rapid development of deep convolutional neural networks (CNNs), super-resolution
(SR) in hyperspectral image (HSI) has achieved good results. Current methods generally …
(SR) in hyperspectral image (HSI) has achieved good results. Current methods generally …
Recurrent homography estimation using homography-guided image warping and focus transformer
We propose the Recurrent homography estimation framework using Homography-guided
image Warping and Focus transformer (FocusFormer), named RHWF. Both being …
image Warping and Focus transformer (FocusFormer), named RHWF. Both being …