Rank-based camera spectral sensitivity estimation under multiple illuminations

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.