López-Alvarez Miguel A, Hernández-Andrés Javier, Romero Javier, Lee Raymond L
Departamento de Optica, Facultad de Ciencias, Universidad de Granada, 18071 Granada, Spain.
Appl Opt. 2005 Sep 20;44(27):5688-95. doi: 10.1364/ao.44.005688.
In earlier work [J. Opt. Soc. Am. A 21, 13-23 (2004)], we showed that a combination of linear models and optimum Gaussian sensors obtained by an exhaustive search can recover daylight spectra reliably from broadband sensor data. Thus our algorithm and sensors could be used to design an accurate, relatively inexpensive system for spectral imaging of daylight. Here we improve our simulation of the multispectral system by (1) considering the different kinds of noise inherent in electronic devices such as change-coupled devices (CCDs) or complementary metal-oxide semiconductors (CMOS) and (2) extending our research to a different kind of natural illumination, skylight. Because exhaustive searches are expensive computationally, here we switch to a simulated annealing algorithm to define the optimum sensors for recovering skylight spectra. The annealing algorithm requires us to minimize a single cost function, and so we develop one that calculates both the spectral and colorimetric similarity of any pair of skylight spectra. We show that the simulated annealing algorithm yields results similar to the exhaustive search but with much less computational effort. Our technique lets us study the properties of optimum sensors in the presence of noise, one side effect of which is that adding more sensors may not improve the spectral recovery.
在早期的工作中[《美国光学学会志A》21, 13 - 23 (2004)]，我们表明线性模型与通过穷举搜索获得的最优高斯传感器相结合，能够从宽带传感器数据中可靠地恢复日光光谱。因此，我们的算法和传感器可用于设计一种准确且相对廉价的日光光谱成像系统。在此，我们通过以下方式改进多光谱系统的模拟：(1) 考虑电子设备（如电荷耦合器件(CCD)或互补金属氧化物半导体(CMOS)）中固有的不同类型噪声；(2) 将研究扩展到另一种自然光照——天光。由于穷举搜索计算成本高昂，在此我们改用模拟退火算法来确定用于恢复天光谱的最优传感器。退火算法要求我们最小化一个单一的代价函数，因此我们开发了一个能计算任意两天光谱的光谱相似度和色度相似度的函数。我们表明，模拟退火算法产生的结果与穷举搜索相似，但计算量要少得多。我们的技术使我们能够研究存在噪声时最优传感器的特性，其一个副作用是增加更多传感器可能并不会改善光谱恢复效果。
