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 …

Capturing high-dimensional (HD) data is a long-term challenge in signal processing and
related fields. Snapshot compressive imaging (SCI) uses a 2D detector to capture HD (≥ …

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

Many learning-based algorithms have been developed to solve the inverse problem of
coded aperture snapshot spectral imaging (CASSI). However, CNN-based methods show …

In coded aperture snapshot spectral imaging (CASSI) system, the real-world hyperspectral
image (HSI) can be reconstructed from the captured compressive image in a snapshot …

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

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 …

Hyperspectral recovery from a single RGB image has seen a great improvement with the
development of deep convolutional neural networks (CNNs). In this paper, we propose two …

With climate change and ever-increasing population growth, the pace of varietal
development needs to be accelerated in order to feed a population of 10 billion by 2050 …

Hyperspectral imaging is an essential imaging modality for a wide range of applications,
especially in remote sensing, agriculture, and medicine. Inspired by existing hyperspectral …