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Image Sensor Modeling: Color Measurement at Low Light Levels

机译：图像传感器建模：弱光下的色彩测量

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摘要

The investigation of low light imaging is of high importance in the field of color science from different perspectives. One of the most important challenges that arises at low light levels is the issue of noise or, more generally speaking, low signal-to-noise ratio (SNR). In the present work, effects of different image sensor noises, such as photon noise, dark current noise, read noise, and quantization error are investigated in low light color measurements. In this regard, a typical image sensor is modeled and employed for this study. A detailed model of noise is considered in the process of implementing the image sensor model to guarantee the precision of the results. Several experiments have been performed over the implemented framework and the results show the following: first, photon noise, read noise, and quantization error lead to uncertain measurements distributed around the noise free measurements and these noisy samples form an elliptical shape in the chromaticity diagram; second, even for an ideal image sensor, in very dark situations, stable measurement of color is impossible due to the physical limitation imposed by the fluctuations in photon emission rate; third, dark current noise reveals dynamic effects on color measurements by shifting their chromaticities towards the chromaticity of the camera black point; fourth, dark current dominates the other sensor noise types in the image sensor in terms of affecting measurements. Moreover, an SNR sensitivity analysis against the noise parameters is presented over different light intensities.
机译：从不同的角度出发，低光成像的研究在色彩科学领域具有重要意义。在低光照水平下出现的最重要挑战之一是噪声问题，或更普遍地说，是低信噪比（SNR）问题。在当前的工作中，在低光颜色测量中研究了不同图像传感器噪声（例如光子噪声，暗电流噪声，读取噪声和量化误差）的影响。在这方面，典型的图像传感器被建模并用于本研究。在实施图像传感器模型的过程中会考虑详细的噪声模型，以确保结果的准确性。在已实现的框架上进行了几次实验，结果表明：首先，光子噪声，读取噪声和量化误差导致不确定的测量分布在无噪声测量周围，并且这些有噪声的样本在色度图中形成椭圆形。其次，即使对于理想的图像传感器，在非常暗的情况下，由于光子发射速率波动所造成的物理限制，也无法稳定地测量颜色；第三，暗电流噪声通过将色度移向相机黑点的色度，揭示了对色彩测量的动态影响；第四，就影响测量而言，暗电流在图像传感器中的其他传感器噪声类型中占主导地位。此外，在不同的光强度下，针对噪声参数进行了SNR灵敏度分析。

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  • 来源
    《Journal of Imaging Science and Technology》 |2014年第3期|030401.1-030401.11|共11页
  • 作者

    Mehdi Rezagholizadeh; James J. Clark;

  • 作者单位

    Centre for Intelligent Machines, McGill University, 3480 University Street, Montreal, Quebec, Canada, H3A 0E9;

    Centre for Intelligent Machines, McGill University, 3480 University Street, Montreal, Quebec, Canada, H3A 0E9;

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