Spectral super-resolution meets deep learning: Achievements and challenges
Spectral super-resolution (sSR) is a very important technique to obtain hyperspectral images
from only RGB images, which can effectively overcome the high acquisition cost and low …
from only RGB images, which can effectively overcome the high acquisition cost and low …
A self-supervised remote sensing image fusion framework with dual-stage self-learning and spectral super-resolution injection
Pan-sharpening is a very productive technique to enhance the spatial details of multispectral
images with the aid of panchromatic images. Nowadays, deep learning-based pan …
images with the aid of panchromatic images. Nowadays, deep learning-based pan …
Drcr net: Dense residual channel re-calibration network with non-local purification for spectral super resolution
Spectral super resolution (SSR) aims to reconstruct the 3D hyperspectral signal from a 2D
RGB image, which is prosperous with the proliferation of Convolutional Neural Networks …
RGB image, which is prosperous with the proliferation of Convolutional Neural Networks …
HASIC-Net: Hybrid attentional convolutional neural network with structure information consistency for spectral super-resolution of RGB images
Spectral super-resolution (SSR), referring to the recovery of a reasonable hyperspectral
image (HSI) from a single RGB image, has achieved satisfactory performance as part of the …
image (HSI) from a single RGB image, has achieved satisfactory performance as part of the …
Spectral reconstruction network from multispectral images to hyperspectral images: A multitemporal case
Hyperspectral (HS) satellite data have been widely applied in many fields due to its
numerous bands. Along with the advantages of high spectral resolution, HS satellite data …
numerous bands. Along with the advantages of high spectral resolution, HS satellite data …
Multi-task interaction learning for spatiospectral image super-resolution
High spatial resolution and high spectral resolution images (HR-HSIs) are widely applied in
geosciences, medical diagnosis, and beyond. However, how to get images with both high …
geosciences, medical diagnosis, and beyond. However, how to get images with both high …
Deep posterior distribution-based embedding for hyperspectral image super-resolution
In this paper, we investigate the problem of hyperspectral (HS) image spatial super-
resolution via deep learning. Particularly, we focus on how to embed the high-dimensional …
resolution via deep learning. Particularly, we focus on how to embed the high-dimensional …
Cost-efficient coupled learning methods for recovering near-infrared information from RGB signals: Application in precision agriculture
Multispectral imaging and the derived spectral analysis offer useful tools for revealing
beneficial information for a variety of applications, eg, precision agriculture, medical imaging …
beneficial information for a variety of applications, eg, precision agriculture, medical imaging …
Multi-sensor multispectral reconstruction framework based on projection and reconstruction
The scarcity and low spatial resolution of hyperspectral images (HSIs) have become a major
problem limiting the application of the images. In recent years, spectral reconstruction (SR) …
problem limiting the application of the images. In recent years, spectral reconstruction (SR) …
Repcpsi: Coordinate-preserving proximity spectral interaction network with reparameterization for lightweight spectral super-resolution
Existing remarkable models for spectral super-resolution (SSR) achieve higher precision at
the expense of computations with larger parameters. These algorithms require the heavy …
the expense of computations with larger parameters. These algorithms require the heavy …