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Evaluation of Color Error and Noise on Simulated Images

机译:在模拟图像上评估色彩误差和噪点

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The evaluation of CMOS sensors performance in terms of color accuracy and noise is a big challenge for camera phone manufacturers. On this paper, we present a tool developed with Matlab a,t STMicroelectronics which allows quality parameters to be evaluated on simulated images. These images are computed based on measured or predicted Quantum Efficiency (QE) curves and noise model. By setting the parameters of integration time and illumination, the tool optimizes the color correction matrix (CCM) and calculates the color error, color saturation and signal-to-noise ratio (SNR). After this color correction optimization step, a Graphics User Interface (GUI) has been designed to display a simulated image at a chosen illumination level, with all the characteristics of a real image taken by the sensor with the previous color correction. Simulated images can be a synthetic Macbeth ColorChecker, for which reflectance of each patch is known, or a multi-spectral image, described by the reflectance spectrum of each pixel or an image taken at high-light level. A validation of the results has been performed with ST under development sensors. Finally we present two applications one based on the trade-offs between color saturation and noise by optimizing the CCM and the other based on demosaicking SNR trade-offs.
机译:对于彩色照相手机制造商来说,就色彩精度和噪声而言,对CMOS传感器性能的评估是一个巨大的挑战。在本文中,我们介绍了由Matlab和STMicroelectronics开发的工具,该工具可以在模拟图像上评估质量参数。这些图像是基于测量或预测的量子效率(QE)曲线和噪声模型计算的。通过设置积分时间和照明参数,该工具可以优化色彩校正矩阵(CCM),并计算色彩误差,色彩饱和度和信噪比(SNR)。在此颜色校正优化步骤之后,已设计了图形用户界面(GUI)以在选定的照明水平下显示模拟图像,并具有传感器在先前颜色校正下所拍摄的真实图像的所有特征。模拟图像可以是合成的Macbeth ColorChecker,每个贴片的反射率已知,也可以是由每个像素的反射光谱描述的多光谱图像或在高光下拍摄的图像。使用开发传感器下的ST进行了结果验证。最后,我们提出两种应用,一种基于通过优化CCM的色彩饱和度和噪声之间的权衡,另一种基于去马赛克的SNR权衡。

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