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Recovering sensor spectral sensitivity from raw data

[+] Author Affiliations
Denglu Wu

Chinese Academy of Sciences, Shenyang Institute of Automation, State Key Laboratory of Robotics, Shenyang 110016, China

University of Chinese Academy of Sciences, Beijing 100039, China

Jiandong Tian

Chinese Academy of Sciences, Shenyang Institute of Automation, State Key Laboratory of Robotics, Shenyang 110016, China

Bingfeng Li

Chinese Academy of Sciences, Shenyang Institute of Automation, State Key Laboratory of Robotics, Shenyang 110016, China

University of Chinese Academy of Sciences, Beijing 100039, China

Yaonan Wang

Hunan University, The College of Electrical and Information, Changsha 410082, China

Yandong Tang

Chinese Academy of Sciences, Shenyang Institute of Automation, State Key Laboratory of Robotics, Shenyang 110016, China

J. Electron. Imaging. 22(2), 023032 (Jun 21, 2013). doi:10.1117/1.JEI.22.2.023032
History: Received November 1, 2012; Revised May 20, 2013; Accepted June 3, 2013
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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.

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Topics

Cameras ; Sensors

Citation

Denglu Wu ; Jiandong Tian ; Bingfeng Li ; Yaonan Wang and Yandong Tang
"Recovering sensor spectral sensitivity from raw data", J. Electron. Imaging. 22(2), 023032 (Jun 21, 2013). ; http://dx.doi.org/10.1117/1.JEI.22.2.023032


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