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 …

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 …

Hyperspectral remote sensing is well-known for its extraordinary spectral distinguishability to
discriminate different materials. However, the cost of hyperspectral image (HSI) acquisition …

Hyperspectral imaging enables many versatile applications for its competence in capturing
abundant spatial and spectral information, which is crucial for identifying substances …

Spectral super-resolution, which reconstructs a hyperspectral image (HSI) from a single red-
green-blue (RGB) image, has acquired more and more attention. Recently, convolution …

Hyperspectral imagery (HSI) contains rich spectral information, which is beneficial to many
tasks. However, acquiring HSI is difficult because of the limitations of current imaging …

Many applications have benefited from the images with both high spatial and spectral
resolution, such as mineralogy and surveillance. However, it is difficult to acquire such …