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 …

This paper focuses on a training-based method to reconstruct a scene's spectral reflectance
from a single RGB image captured by a camera with known spectral response. In particular …

Hyperspectral (HS) reconstruction from RGB images denotes the recovery of whole-scene
HS information, which has attracted much attention recently. State-of-the-art approaches …

Most of spectral estimation methods are based on improving the learning‐based procedures
which mainly modify the training sets used by the basic methods. In this article, a new …

Two regression methods, namely, Support Vector Regression (SVR) and Kernel Ridge
Regression (KRR), are used to reconstruct the spectral reflectance curves of samples of …

A high-spatial-resolution hyperspectral image (HR-HSI) can be obtained by fusing a
hyperspectral image (HSI) and a multispectral image (HSI-MSI) since it takes the advantage …

A hyperspectral camera captures a scene in many frequency bands across the spectrum,
providing rich information and facilitating numerous applications. The potential of …

The spectral reflectance of an object surface provides valuable information of its
characteristics. Reflectance reconstruction from multispectral image data is typically based …

The positive basis functions of the reflectance spectra of Munsell color chips are extracted by
using the classical nonnegative matrix factorization method. Different numbers of basis, ie, 3 …

The spectral super-resolution (SpeSR) from multispectral images (MSIs) to hyperspectral
images (HSIs) can bring rich spectral information. The deep learning-based methods have …