Rank-based camera spectral sensitivity estimation under multiple illuminations

Bowen Xu, Long Ma, Peng Li
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引用次数: 1

Abstract

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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多照度下基于秩的相机光谱灵敏度估计
光谱灵敏度函数是数码相机的一个重要参数,而相机光谱灵敏度函数的恢复是一个重要的研究课题。在本文中,我们提出了一种新的基于秩的约束算法来估计光谱灵敏度。根据不同照度下数码相机成像标准色样响应值的阶数,对光谱灵敏度的估计施加了约束。颜色样本和照度在估计过程中是已知的。算法中有两种排序约束,一种是单光照下的排序约束,另一种是多光照下的排序约束。此外,在两个排序约束的支持下,我们在实验中使用了更少的颜色样本。通过几个数值模拟实验对研究结果进行了验证,并与其他光谱灵敏度估计算法进行了比较。我们添加了不同程度的噪声,并尝试了多种照明的不同组合,以恢复不同相机的光谱灵敏度。实验结果表明,该算法能较好地估计相机光谱灵敏度函数,减少了计算量。同时,使用较少的颜色样本可以在不增加实验误差度量的情况下降低实验的复杂性。
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