Authors:
Yusuke Murayama
;
Pengchang Zhang
and
Ari Ide-Ektessabi
Affiliation:
Kyoto University, Japan
Keyword(s):
Camera Characterization, Bayesian Estimation, Marginalized Likelihood, Spectral Sensitivity, Linearization.
Related
Ontology
Subjects/Areas/Topics:
Computational Photography
;
Computer Vision, Visualization and Computer Graphics
;
Device Calibration, Characterization and Modeling
;
Image Formation and Preprocessing
;
Image Formation, Acquisition Devices and Sensors
;
Rendering
Abstract:
We proposed a new practical method for identifying characteristics of a color digital camera: spectral sensitivity function, linearization function and noise variance of each color channel. The only input is an image of a color chart acquired by the objective camera with a spectral-content-known illuminant, and the camera characteristics are obtained automatically. The proposed method was developed in the Bayesian statistical framework in order to improve upon previous methods, namely, to eliminate trial-and-error parameter tuning and to identify linearization function as well as spectral sensitivities. The polyline linearization function and the noise variance of a color channel were considered as hyperparameters, and estimated by the marginalized likelihood criterion. Such hyperparameters associated with the smoothness of the sensitivity curves were also estimated similarly. Then the spectral sensitivity of a color channel was obtained as maximum a posteriori solution. In experimen
ts using synthetic data, the proposed method was found to be widely adaptable to the forms of sensitivity curves and the levels of sensor noise.
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