IEICE TRANSACTIONS on Information
Computationally Efficient Reflectance Estimation for Hyperspectral Images
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.
- Publication
- IEICE TRANSACTIONS on Information 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
Authors
Takaaki OKABE
The University of Kitakyushu
Masahiro OKUDA
The University of Kitakyushu
Keyword
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Takaaki OKABE, Masahiro OKUDA, "Computationally Efficient Reflectance Estimation for Hyperspectral Images" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 9, pp. 2253-2256, September 2017, doi: 10.1587/transinf.2017EDL8051.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDL8051/_p
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@ARTICLE{e100-d_9_2253,
author={Takaaki OKABE, Masahiro OKUDA, },
journal={IEICE TRANSACTIONS on Information},
title={Computationally Efficient Reflectance Estimation for Hyperspectral Images},
year={2017},
volume={E100-D},
number={9},
pages={2253-2256},
abstract={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.},
keywords={},
doi={10.1587/transinf.2017EDL8051},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Computationally Efficient Reflectance Estimation for Hyperspectral Images
T2 - IEICE TRANSACTIONS on Information
SP - 2253
EP - 2256
AU - Takaaki OKABE
AU - Masahiro OKUDA
PY - 2017
DO - 10.1587/transinf.2017EDL8051
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2017
AB - 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.
ER -
English
