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Journal of Electronic Imaging

Recovering sensor spectral sensitivity from raw data
Author(s): Denglu Wu; Jiandong Tian; Bingfeng Li; Yaonan Wang; Yandong Tang
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Paper Abstract

Accurate sensor spectral sensitivity (SSS) plays an important role in color reproduction, imaging, and computer vision. However, such data are not always available from manufacturers. We focus on recovering SSS from raw data, and propose a method to recover SSS. Using raw data, instead of sRGB data, to recover the SSS is due to the fact that the raw data are more closely related with actual sensor response values than sRGB data. With the raw data, the SSS can be recovered more accurately than with sRGB data that is widely used to recover SSS by almost all state-of-art recovery methods. Besides applying raw data in the recovery process, the other contribution is that we propose a new and simple cost function for the SSS recovery problem. This problem is then transformed into an optimization issue by minimizing the cost function. This minimization can be solved by a multiobject optimization method with a genetic algorithm. In contrast to the previous methods, our method is simple and has no parameter that needs to be determined by some prior knowledge. We validate our method through the experiments and comparison with the state-of-art methods under different illuminants.

Paper Details

Date Published: 21 June 2013
PDF: 9 pages
J. Electron. Imaging. 22(2) 023032 doi: 10.1117/1.JEI.22.2.023032
Published in: Journal of Electronic Imaging Volume 22, Issue 2
Show Author Affiliations
Denglu Wu, Shenyang Institute of Automation (China)
Jiandong Tian, Shenyang Institute of Automation (China)
Bingfeng Li, Shenyang Institute of Automation (China)
Yaonan Wang, Hunan Univ. (China)
Yandong Tang, Shenyang Institute of Automation (China)


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