Human vision noise model validation for the U.S. Army sensor performance metric

Bradley L. Preece; Jeffrey T. Olson; Joseph P. Reynolds; Jonathan D. Fanning; David P. Haefner

doi:10.1117/1.OE.53.6.061712

28 April 2014 Human vision noise model validation for the U.S. Army sensor performance metric

Bradley L. Preece, Jeffrey T. Olson, Joseph P. Reynolds, Jonathan D. Fanning, David P. Haefner

Author Affiliations +

Optical Engineering, Vol. 53, Issue 6, 061712 (April 2014). https://doi.org/10.1117/1.OE.53.6.061712

Abstract

Image noise originating from a sensor system is often the limiting factor in target acquisition performance, especially when limited by atmospheric transmission or low-light conditions. To accurately predict target acquisition range performance for a wide variety of imaging systems, image degradation introduced by the sensor must be properly combined with the limitations of the human visual system (HVS). This crucial step of incorporating the HVS has been improved and updated within NVESD’s latest imaging system performance model. The new noise model discussed here shows how an imaging system’s noise and blur are combined with the contrast threshold function (CTF) to form the system CTF. Model calibration constants were found by presenting low-contrast sine gratings with additive noise in a two alternative forced choice experiment. One of the principal improvements comes from adding an eye photon noise term allowing the noise CTF to be accurate over a wide range of luminance. The latest HVS noise model is then applied to the targeting task performance metric responsible for predicting system performance from the system CTF. To validate this model, human target acquisition performance was measured from a series of infrared and visible-band noise-limited imaging systems.

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Bradley L. Preece, Jeffrey T. Olson, Joseph P. Reynolds, Jonathan D. Fanning, and David P. Haefner "Human vision noise model validation for the U.S. Army sensor performance metric," Optical Engineering 53(6), 061712 (28 April 2014). https://doi.org/10.1117/1.OE.53.6.061712

Published: 28 April 2014

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