Stress in forest ecosystems (FES) occurs as a result of land-use intensification, disturbances,
resource limitations or unsustainable management, causing changes in forest health (FH) at …

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 …

Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from
a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system. The …

The rapid development of deep learning provides a better solution for the end-to-end
reconstruction of hyperspectral image (HSI). However, existing learning-based methods …

In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral
image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from …

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

Imaging depth and spectrum have been extensively studied in isolation from each other for
decades. Recently, hyperspectral-depth (HS-D) imaging emerges to capture both …

We present a novel hyperspectral image reconstruction algorithm, which overcomes the
long-standing tradeoff between spectral accuracy and spatial resolution in existing …

Regularization is a fundamental technique to solve an ill-posed optimization problem
robustly and is essential to reconstruct compressive hyperspectral images. Various hand …

This paper presents a unified deep learning framework to recover hyperspectral images
from spectrally undersampled projections. Specifically, we investigate two kinds of …