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Numerical Demultiplexing of Color Image Sensor Measurements via Non-linear Random Forest Modeling

机译:通过非线性随机森林建模对彩色图像传感器测量值进行数值多路分解

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摘要

Due to recent advancements in technology, consumer digital cameras are becoming cheaper and easier to use. These consumer digital cameras, with Bayer color filter arrays (CFAs), allow for simultaneous capture of the red, green and blue (RGB) channels. To achieve higher spectral resolution, multispectral imaging systems use methods such as filter wheels and tunable filters to capture data in a sequential manner. However, in order to capture transient phenomena, one would need to capture spectral information of a 2D scene in a simultaneous manner. Therefore, there has been an on-going trend towards creating a simultaneous multispectral imaging system that uses a conventional consumer digital camera with a Bayer CFA.Such a system allows for a effective imaging of transient or dynamic phenomena with a low-cost and compact system.Currently, the main method to accomplish this is known as Wiener estimation which uses statistical assumptions of the relationship between the incoming spectra and the RGB measurements. However, these assumptions limit the ability to accurately predict the incoming spectra. Therefore, we leverage a comprehensive framework based on numerical demultiplexing of sensor measurements via spectral characterization of the image sensor CFA and non-linear random forest modeling.To create this numerical demultiplexing system we create a forward model from the spectral sensitivity of the imaging system, which is accomplished with a monochrometer.This forward model is then used to create a mapping of 10,000 randomly generated spectra to their corresponding RGB values.This mapping acts as our training set for our non-linear inverse model which utilizes the random forest modeling framework.Having constructed the numerical demultiplexer, we test the performance against the state-of-the-art Wiener estimation for both quantitative and qualitative experiments.In the first set of experiments, we performed a quantitative performance assessment of the proposed framework within a controlled simulation environment.The second set of experiments, validated the observations made from thefirst set of controlled simulation experiments within a real-world setting. More specifically, we used an icon with different colors as well as a scene of different color flowers to perform quantitative analysis.In these experiments, we show that the proposed numerical demultiplexer outperforms the state-of-the art and is a more robust and reliable way to infer higher spectra from RGB measurements.Having validated the numerical demultiplexer, we use it for two applications which are photoplethysmogrpahic imaging and multispectral microscopy.For photoplethysmogrpahic imaging we found that decomposing the RGB camera measurements into narrow-band spectral information can noticeably improve the prediction of heart rate estimation. In addition, we used the numerical demultiplexer for both a bright-field multispectral microscope as well as a dark-field fluorescence multispectral microscope, which illustrates its potential as a low-cost, portable, point-of-care system.
机译:由于技术的最新发展,消费类数码相机变得越来越便宜和易于使用。这些带有拜耳彩色滤光片阵列(CFA)的消费类数码相机可以同时捕获红色,绿色和蓝色(RGB)通道。为了获得更高的光谱分辨率,多光谱成像系统使用诸如滤光轮和可调滤光器之类的方法以顺序方式捕获数据。但是,为了捕获瞬态现象,将需要同时捕获2D场景的光谱信息。因此,使用同时具有拜耳CFA的传统消费类数码相机的同步多光谱成像系统的开发一直存在一种趋势,这种系统可以通过低成本,紧凑的系统对瞬态或动态现象进行有效成像。当前,实现此目的的主要方法称为维纳估计,该方法使用入射光谱和RGB测量之间关系的统计假设。但是,这些假设限制了准确预测入射光谱的能力。因此,我们利用基于图像传感器CFA的光谱表征和非线性随机森林建模对传感器测量值进行数字多路分解的综合框架。要创建此数字多路分解系统,我们将根据成像系统的光谱灵敏度创建一个正向模型,然后使用此正向模型创建10,000个随机生成的光谱到其对应RGB值的映射,此映射充当我们利用随机森林建模框架的非线性逆模型的训练集。构建了数字多路分解器之后,我们针对最新的Wiener估计在定量和定性实验中测试了性能。在第一组实验中,我们在受控的模拟环境中对提出的框架进行了定量性能评估第二组实验,验证了从在真实环境中的第一组受控模拟实验。更具体地说,我们使用了具有不同颜色的图标以及具有不同颜色花朵的场景来进行定量分析。在这些实验中,我们证明了所提出的数字多路分解器优于最新技术,并且更加健壮和可靠从RGB测量中推断出更高光谱的方法。在验证了数字多路分解器之后,我们将其用于光体积描记成像和多光谱显微镜这两个应用程序中。对于光体积描记成像,我们发现将RGB相机测量值分解为窄带光谱信息可以显着改善心率估计的预测。此外,我们将数值解复用器用于明场多光谱显微镜和暗场荧光多光谱显微镜,这说明了其作为低成本,便携式,即时医疗系统的潜力。

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    Deglint Jason;

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  • 年度 2016
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