Impacting factors on camera calibration for spectral sensitivity estimation


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The acquisition of camera spectral sensitivity has become a hot issue in recent years because of its fundamental role in image processing and color reproduction. Although the estimation method based on camera response formation model has many advantages, the implementation procedures for spectral characterization are still tedious and time-consuming. In order for the improvement of calibration efficiency, it was investigated in this study that how the changes of the training samples selection would influence the estimation results and how many color samples at least should be included to achieve a high-fidelity color reproduction.

The choice of the illuminants was also discussed to test whether the accuracy of the estimated spectra would degrade at the wavelength ranges with relatively low SPD of illuminant. Differing from the previous methods using relative errors in camera RGB space as cost function, we combine the colorimetric characterization with the spectral sensitivity estimation so as to minimize the estimation errors in device-independent color space like CIE XYZ. The detailed comparisons indicate that this modification could produce better perceived performance.


Figure 1. The mean color difference, along with the standard deviation, of the ($96-k$) testing samples.
Figure 2. The color difference of the 24 testing samples under illuminant A with the training data obtained under D65.
Figure 3. The color difference of the 24 testing samples under D65 with the training data under illuminant A.



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