Computationally Efficient Reflectance Estimation for Hyperspectral Images

Takaaki OKABE  Masahiro OKUDA  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.9   pp.2253-2256
Publication Date: 2017/09/01
Publicized: 2017/05/26
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDL8051
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
Keyword: 
hyperspectral image,  retinex,  reflectance estimation,  singular values,  

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Summary: 
The Retinex theory assumes that large intensity changes correspond to reflectance edges, while smoothly-varying regions are due to shading. Some algorithms based on the theory adopt simple thresholding schemes and achieve adequate results for reflectance estimation. In this paper, we present a practical reflectance estimation technique for hyperspectral images. Our method is realized simply by thresholding singular values of a matrix calculated from scaled pixel values. In the method, we estimate the reflectance image by measuring spectral similarity between two adjacent pixels. We demonstrate that our thresholding scheme effectively estimates the reflectance and outperforms the Retinex-based thresholding. In particular, our methods can precisely distinguish edges caused by reflectance change and shadows.