Many vision tasks such as scene segmentation, or the recognition of materials within a
scene, become considerably easier when it is possible to measure the spectral reflectance …

Capturing hyperspectral images requires expensive and specialized hardware that is not
readily accessible to most users. Digital cameras, on the other hand, are significantly …

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

Acquisition of spectral information of objects being imaged through the use of sensor
responses is important to reproduce color images under various illuminations. In the past …

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

Principal component analysis (PCA) is widely used to reconstruct the spectral reflectance of
surface colors. However, the estimated spectral accuracy is low when using only one set of …

Linear camera responses are required for recovering the total amount of incident irradiance,
quantitative image analysis, spectral reconstruction from camera responses and …

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

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

In the spectral reconstruction (SR) problem, reflectance and/or radiance spectra are
recovered from RGB images. Most of the prior art only attempts to solve this problem for fixed …