Rank-based camera spectral sensitivity estimation
Rank-based camera spectral sensitivity estimation
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DOI：
10.1364/josaa.33.000589
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发表时间：
2016-04-01
影响因子：
1.9
通讯作者：
Mackiewicz, Michal
中科院分区：
文献类型：
--
作者：
Finlayson, Graham;Darrodi, Maryam Mohammadzadeh;Mackiewicz, Michal
为了准确预测数码相机对光谱刺激的响应，需要知道其传感器的光谱灵敏度函数。这些功能可以通过实验室中的直接测量来确定--这是一个困难而漫长的过程--或者通过简单的统计推断。统计推断方法是基于这样的观察，即当相机对光谱刺激做出线性响应时，器件的光谱灵敏度与相机的RGB响应值线性相关，因此可以通过回归得到。然而，对于渲染的图像，例如由移动电话拍摄的JPEG图像，违反了这一线性假设。当使用回归方法时，即使是微小的线性偏差也会对恢复的光谱灵敏度的准确性产生负面影响。在我们的工作中，我们开发了一种新的相机光谱灵敏度估计技术，该技术可以从线性图像中恢复线性器件的光谱灵敏度，并从渲染图像中恢复有效的线性灵敏度。根据我们的方法，一对响应的排序对(传感器的)潜在光谱灵敏度曲线的形状施加了约束。从技术上讲，每个秩对将底层传感器可能所在的空间分成两部分(可行域和不可行域)。通过将所有排序对的可行域相交，我们可以找到一个传感器空间的可行域。实验证明，使用秩序可以提供与现有技术相同的估计。然而，当数据不是线性的时，基于等级的方法提供了估计性能的阶梯变化，并且第一次允许估计甚至可能没有“RAW模式”的设备的有效灵敏度。实验验证了该方法的有效性。(C)2016年度美国光学学会
In order to accurately predict a digital camera response to spectral stimuli, the spectral sensitivity functions of its sensor need to be known. These functions can be determined by direct measurement in the lab-a difficult and lengthy procedure-or through simple statistical inference. Statistical inference methods are based on the observation that when a camera responds linearly to spectral stimuli, the device spectral sensitivities are linearly related to the camera rgb response values, and so can be found through regression. However, for rendered images, such as the JPEG images taken by a mobile phone, this assumption of linearity is violated. Even small departures from linearity can negatively impact the accuracy of the recovered spectral sensitivities, when a regression method is used. In our work, we develop a novel camera spectral sensitivity estimation technique that can recover the linear device spectral sensitivities from linear images and the effective linear sensitivities from rendered images. According to our method, the rank order of a pair of responses imposes a constraint on the shape of the underlying spectral sensitivity curve (of the sensor). Technically, each rank-pair splits the space where the underlying sensor might lie in two parts (a feasible region and an infeasible region). By intersecting the feasible regions from all the ranked-pairs, we can find a feasible region of sensor space. Experiments demonstrate that using rank orders delivers equal estimation to the prior art. However, the Rank-based method delivers a step-change in estimation performance when the data is not linear and, for the first time, allows for the estimation of the effective sensitivities of devices that may not even have "raw mode." Experiments validate our method. (C) 2016 Optical Society of America