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 …

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 …

Computational hyperspectral imaging (CHI) is a cutting-edge technique, which plays a
pivotal role in breaking through the quality bottleneck of hyperspectral images (HSI). Among …

Existing leading methods for spectral reconstruction (SR) focus on designing deeper or
wider convolutional neural networks (CNNs) to learn the end-to-end map** from the RGB …

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 …

Modern hyperspectral imaging systems produce huge datasets potentially conveying a great
abundance of information; such a resource, however, poses many challenges in the …

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 …

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 …

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 …

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 …