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 …
Deep learning meets hyperspectral image analysis: A multidisciplinary review
Modern hyperspectral imaging systems produce huge datasets potentially conveying a great
abundance of information; such a resource, however, poses many challenges in the …
abundance of information; such a resource, however, poses many challenges in the …
End-to-end low cost compressive spectral imaging with spatial-spectral self-attention
Coded aperture snapshot spectral imaging (CASSI) is an effective tool to capture real-world
3D hyperspectral images. While a number of existing work has been conducted for …
3D hyperspectral images. While a number of existing work has been conducted for …
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 …
l-net: Reconstruct hyperspectral images from a snapshot measurement
We propose the l-net, which reconstructs hyperspectral images (eg, with 24 spectral
channels) from a single shot measurement. This task is usually termed snapshot …
channels) from a single shot measurement. This task is usually termed snapshot …
Ntire 2020 challenge on spectral reconstruction from an rgb image
This paper reviews the second challenge on spectral reconstruction from RGB images, ie,
the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image …
the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image …
Adaptive weighted attention network with camera spectral sensitivity prior for spectral reconstruction from RGB images
Recent promising effort for spectral reconstruction (SR) focuses on learning a complicated
mapping through using a deeper and wider convolutional neural networks (CNNs) …
mapping through using a deeper and wider convolutional neural networks (CNNs) …
Hierarchical regression network for spectral reconstruction from RGB images
Capturing visual image with a hyperspectral camera has been successfully applied to many
areas due to its narrow-band imaging technology. Hyperspectral reconstruction from RGB …
areas due to its narrow-band imaging technology. Hyperspectral reconstruction from RGB …
Learning hyperspectral images from RGB images via a coarse-to-fine CNN
Hyperspectral remote sensing is well-known for its extraordinary spectral distinguishability to
discriminate different materials. However, the cost of hyperspectral image (HSI) acquisition …
discriminate different materials. However, the cost of hyperspectral image (HSI) acquisition …
A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging
Hyperspectral imaging enables many versatile applications for its competence in capturing
abundant spatial and spectral information, which is crucial for identifying substances …
abundant spatial and spectral information, which is crucial for identifying substances …