Spectral imaging with deep learning
The goal of spectral imaging is to capture the spectral signature of a target. Traditional
scanning method for spectral imaging suffers from large system volume and low image …
scanning method for spectral imaging suffers from large system volume and low image …
Methods for image denoising using convolutional neural network: a review
AE Ilesanmi, TO Ilesanmi - Complex & Intelligent Systems, 2021 - Springer
Image denoising faces significant challenges, arising from the sources of noise. Specifically,
Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in …
Gaussian, impulse, salt, pepper, and speckle noise are complicated sources of noise in …
Multispectral and hyperspectral image fusion in remote sensing: A survey
G Vivone - Information Fusion, 2023 - Elsevier
The fusion of multispectral (MS) and hyperspectral (HS) images has recently been put in the
spotlight. The combination of high spatial resolution MS images with HS data showing a …
spotlight. The combination of high spatial resolution MS images with HS data showing a …
Ntire 2022 spectral recovery challenge and data set
This paper reviews the third biennial challenge on spectral reconstruction from RGB images,
ie, the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB …
ie, the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB …
Hyperspectral image super-resolution via deep spatiospectral attention convolutional neural networks
Hyperspectral images (HSIs) are of crucial importance in order to better understand features
from a large number of spectral channels. Restricted by its inner imaging mechanism, the …
from a large number of spectral channels. Restricted by its inner imaging mechanism, the …
Non-local meets global: An iterative paradigm for hyperspectral image restoration
Non-local low-rank tensor approximation has been developed as a state-of-the-art method
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …
for hyperspectral image (HSI) restoration, which includes the tasks of denoising …
GuidedNet: A general CNN fusion framework via high-resolution guidance for hyperspectral image super-resolution
Hyperspectral image super-resolution (HISR) is about fusing a low-resolution hyperspectral
image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to generate a high …
image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to generate a high …
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 …
Spatial-spectral structured sparse low-rank representation for hyperspectral image super-resolution
Hyperspectral image super-resolution by fusing high-resolution multispectral image (HR-
MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution …
MSI) and low-resolution hyperspectral image (LR-HSI) aims at reconstructing high resolution …
Sparse recovery of hyperspectral signal from natural RGB images
B Arad, O Ben-Shahar - Computer Vision–ECCV 2016: 14th European …, 2016 - Springer
Hyperspectral imaging is an important visual modality with growing interest and range of
applications. The latter, however, is hindered by the fact that existing devices are limited in …
applications. The latter, however, is hindered by the fact that existing devices are limited in …