Image Sensor Modeling: Color Measurement at Low Light Levels

Abstract

The investigation of low light imaging is of high importance 1 in the field of color science from different perspectives. One of the 2 most important challenges that arises at low light levels is the issue 3 of noise or, more generally speaking, low signal-to-noise ratio (SNR). 4 In the present work, effects of different image sensor noises, such as 5 photon noise, dark current noise, read noise, and quantization error, 6 are investigated on low light color measurements. In this regard, 7 a typical image sensor is modeled and employed for this study. A 8 detailed model of noise is considered in the process of implementing 9 the image sensor model to guarantee the precision of the results. 10 Several experiments have been performed over the implemented 11 framework and the results show the following: first, photon noise, 12 read noise, and quantization error lead to uncertain measurements 13 distributed around the noise free measurements and these noisy 14 samples form an elliptical shape in the chromaticity diagram; 15 second, even for an ideal image sensor, in very dark situations, 16 stable measurement of color is impossible due to the physical 17 limitation imposed by the fluctuations in photon emission rate; third, 18 dark current noise reveals dynamic effects on color measurements 19 by shifting their chromaticities towards the chromaticity of the 20 camera black point; fourth, dark current dominates the other sensor 21 noise types in the image sensor in terms of affecting measurements. 22 Moreover, an SNR sensitivity analysis against the noise parameters 23 is presented over different light intensities.

Illuminant estimation in multispectral imaging

“…Typically, the main sources of noise are photon shot noise, dark current noise, read noise, and quantization noise [70]. We do not consider photon shot noise and dark current noise since the Foster Dataset 2004 is already corrected for these types of noise.…”