Camera response prediction for various capture settings using the spectral sensitivity and crosstalk model


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In this article, a camera response formation model is proposed to accurately predict the responses of images captured under various exposure settings. Differing from earlier works that estimated the camera relative spectral sensitivity, our model constructs the physical spectral sensitivity curves and device-dependent parameters that convert the absolute spectral radiances of target surfaces to the camera readout responses. With this model the camera responses to miscellaneous combinations of surfaces and illuminants could be accurately predicted, thus creating an “imaging simulator”, by using which the colorimetric and photometric research based on the cameras would be much of convenience.


Figure 1. The L-curve of the regularization. The circle denotes the corner of L-curve, corresponding to the optimal regularization parameter $\lambda$.
Figure 2. The training (plain patches) and testing (hatched patches) samples in our experiments. 44 repeated neutral patches around DSG color checker were discarded through the work.
Figure 3. The spectral radiances of 48 selected training samples in the experiments.
Figure 4. Nikon D3x’s normalized spectral sensitivities estimated by different methods.
Figure 5. Mean CIEDE2000 color difference over 48 training sample under different capture settings.
Figure 6. The distribution of training samples in CIELAB $a^*b^*$ plane, the radius of circle denotes five times of the mean color difference over five capture settings of our model.



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