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9 May 2011Modeling human performance with low light sparse color imagers
Reflective band sensors are often signal to noise limited in low light conditions. Any additional filtering to obtain
spectral information further reduces the signal to noise, greatly affecting range performance. Modern sensors, such as
the sparse color filter CCD, circumvent this additional degradation through reducing the number of pixels affected by
filters and distributing the color information. As color sensors become more prevalent in the warfighter arsenal, the
performance of the sensor-soldier system must be quantified. While field performance testing ultimately validates the
success of a sensor, accurately modeling sensor performance greatly reduces the development time and cost, allowing
the best technology to reach the soldier the fastest. Modeling of sensors requires accounting for how the signal is
affected through the modulation transfer function (MTF) and noise of the system. For the modeling of these new
sensors, the MTF and noise for each color band must be characterized, and the appropriate sampling and blur must be
applied. We show how sparse array color filter sensors may be modeled and how a soldier's performance with such a
sensor may be predicted. This general approach to modeling color sensors can be extended to incorporate all types of
low light color sensors.
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David P. Haefner, Joseph P. Reynolds, Jae Cha, Van Hodgkin, "Modeling human performance with low light sparse color imagers," Proc. SPIE 8014, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXII, 801404 (9 May 2011); https://doi.org/10.1117/12.885152