Spectral imaging collects and processes information along spatial and spectral coordinates
quantified in discrete voxels, which can be treated as a 3D spectral data cube. The spectral …

A coupled deep learning approach for coded aperture design and single-pixel
measurements classification is proposed. A whole neural network is trained to …

Spectral super-resolution has attracted research attention recently, which aims to generate
hyperspectral images from RGB images. However, most of the existing spectral super …

Recently, many deep neural networks (DNN) have been proposed to solve the spectral
reconstruction (SR) problem: recovering spectra from RGB measurements. Most DNNs seek …

Spectral reconstruction (SR) algorithms recover hyperspectral measurements from RGB
camera responses. Statistical models at different levels of complexity are used to solve the …

Spectral reconstruction algorithms recover spectra from RGB sensor responses. Recent
methods—with the very best algorithms using deep learning—can already solve this …

Image fusion is a technique of combining two or more images into a single image which is
more informative from an interpretation point of view. With the rapid development of different …

Spectral reconstruction (SR) algorithms attempt to recover hyperspectral information from
RGB camera responses. Recently, the most common metric for evaluating the performance …

Abstract Recently Convolutional Neural Networks (CNN) have been used to reconstruct
hyperspectral information from RGB images, and this spectral reconstruction problem (SR) …

It has long been a challenge to obtain high spectral and spatial resolution simultaneously for
the field of measurement and detection. Here we present a measurement system based on …