Separating Reflective and Fluorescent Components Using High Frequency Illumination in the Spectral Domain
Abstract
Hyperspectral imaging is beneficial to many applications but current methods do not consider fluorescent effects which are present in everyday items ranging from paper, to clothing, to even our food. Furthermore, everyday fluorescent items exhibit a mix of reflectance and fluorescence. So proper separation of these components is necessary for analyzing them. In this paper, we demonstrate efficient separation and recovery of reflective and fluorescent emission spectra through the use of high frequency illumination in the spectral domain. With the obtained fluorescent emission spectra from our high frequency illuminants, we then present to our knowledge, the first method for estimating the fluorescent absorption spectrum of a material given its emission spectrum. Conventional bispectral measurement of absorption and emission spectra needs to examine all combinations of incident and observed light wavelengths. In contrast, our method requires only two hyperspectral images. The effectiveness of our proposed methods are then evaluated through a combination of simulation and real experiments. We also demonstrate an application of our method to synthetic relighting of real scenes.
Cite
Text
Fu et al. "Separating Reflective and Fluorescent Components Using High Frequency Illumination in the Spectral Domain." International Conference on Computer Vision, 2013. doi:10.1109/ICCV.2013.63Markdown
[Fu et al. "Separating Reflective and Fluorescent Components Using High Frequency Illumination in the Spectral Domain." International Conference on Computer Vision, 2013.](https://mlanthology.org/iccv/2013/fu2013iccv-separating/) doi:10.1109/ICCV.2013.63BibTeX
@inproceedings{fu2013iccv-separating,
title = {{Separating Reflective and Fluorescent Components Using High Frequency Illumination in the Spectral Domain}},
author = {Fu, Ying and Lam, Antony and Sato, Imari and Okabe, Takahiro and Sato, Yoichi},
booktitle = {International Conference on Computer Vision},
year = {2013},
doi = {10.1109/ICCV.2013.63},
url = {https://mlanthology.org/iccv/2013/fu2013iccv-separating/}
}