This paper explores hyperspectral reflectance factor estimation using Gaussian process
regression with multispectral-and trichromatic measurements. Estimations are performed in …

A self-training-based spectral reflectance recovery method was developed to accurately
reconstruct the spectral images of art paintings with multispectral imaging. By partitioning the …

The similarity between samples is an important factor for spectral reflectance recovery. The
current way of selecting samples after dividing dataset does not take subspace merging into …

The accuracy of recovered spectra from camera responses mainly depends on the spectral
estimation algorithm used, the camera and filters selected, and the light source used to …

Spectral reflectance is widely useful in many different applications nowadays. Accurate
spectral images are usually high-dimensional data and larger files, so a proper dimensional …

In the last several years, deep learning has been introduced to recover a hyperspectral
image (HSI) from a single RGB image and demonstrated good performance. In particular …

A new adaptive compressive sensing-based method is proposed for recovering spectral
reflectance from a single RGB image by using dynamic principal components that are …

Previous research has shown that the effectiveness of selecting filter sets from among a
large set of commercial broadband filters by a vector analysis method based on maximum …

We evaluate three link functions (square root, logit, and copula) and Matérn kernel in the
kernel-based estimation of reflectance spectra of the Munsell Matte collection in the 400 …

The performance of learning-based spectral estimation is greatly influenced by the set of
training samples selected to create the reconstruction model. Training sample selection …