The choice of light source affects the accuracy of the spectral sensitivity estimation. In this paper, we propose to estimate the spectral sensitivity function of digital camera using spectrally tunable LED light sources. The spectral power distribution of the LED light source is determined by a combination of multiple LEDs and their weight coefficients. The method of tuning the weight coefficients of the LEDs includes Monte Carlo method and particle swarm optimization algorithm, so that the LED light source with the smallest estimation error is defined as the optimal light source. Experimental results show that the particle swarm algorithm gives the best estimation results. The relative error of estimation using LED light sources is significantly reduced when compared with the results when using a single light source for estimation (e.g., D65 light source).
The spectral sensitivity function of a digital camera is an important parameter and the recovery of camera spectral sensitivity function is a crucial study. In this paper, we propose a new rank-based constraint algorithm to estimate the spectral sensitivity. The constraints are imposed on the estimation of the spectral sensitivity based on the rank orders of the response values of the digital camera for imaging standard color samples under different illuminations. Color samples and illuminations are known in the estimation process. We have two kinds of ranking constraints in the algorithm, one is ranking under a single illumination, and the other is ranking under multiple illuminations. Besides, with the support of two ranking constraints, we use fewer color samples in the experiments. The study is evaluated by several numerical simulation experiments and compared with other spectral sensitivity estimation algorithms. We added various levels of noise and tried various combinations of multiple illuminations to recover the spectral sensitivity of different cameras. The experimental results suggest that the proposed algorithm performs better in estimating the camera spectral sensitivity function and computational work is reduced. At the same time, utilizing fewer color samples can reduce the complexity of the experiment without increasing the experimental error metric.
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