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Proceedings Paper

Modeling human performance with low light sparse color imagers
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Paper Abstract

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.

Paper Details

Date Published: 9 May 2011
PDF: 12 pages
Proc. SPIE 8014, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXII, 801404 (9 May 2011); doi: 10.1117/12.885152
Show Author Affiliations
David P. Haefner, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Joseph P. Reynolds, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Jae Cha, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Van Hodgkin, U.S. Army Night Vision & Electronic Sensors Directorate (United States)


Published in SPIE Proceedings Vol. 8014:
Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXII
Gerald C. Holst; Keith A. Krapels, Editor(s)

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