Efficient reduction of complex noise in passive millimeter-wavelength video utilizing Bayesian surprise

T. Nathan Mundhenk; Josh Baron; Roy M. Matic

doi:10.1117/12.887672

1 June 2011 Efficient reduction of complex noise in passive millimeter-wavelength video utilizing Bayesian surprise

T. Nathan Mundhenk, Josh Baron, Roy M. Matic

Proceedings Volume 8042, Display Technologies and Applications for Defense, Security, and Avionics V; and Enhanced and Synthetic Vision 2011; 804210 (2011) https://doi.org/10.1117/12.887672
Event: SPIE Defense, Security, and Sensing, 2011, Orlando, Florida, United States

Abstract

Passive millimeter wavelength (PMMW) video holds great promise given its ability to see targets and obstacles through fog, smoke and rain. However, current imagers produce undesirable complex noise. This can come as a mixture of fast shot (snow like) noise and a slower forming circular fixed pattern. Shot noise can be removed by a simple gain style filter. However, this can produce blurring of objects in the scene. To alleviate this, we measure the amount of Bayesian surprise in videos. Bayesian surprise is feature change in time which is abrupt, but cannot be accounted for as shot noise. Surprise is used to attenuate the shot noise filter in locations of high surprise. Since high Bayesian surprise in videos is very salient to observers, this reduces blurring particularly in places where people visually attend. Fixed pattern noise is removed after the shot noise using a combination of Non-uniformity correction (NUC) and Eigen Image Wavelet Transformation. The combination allows for online removal of time varying fixed pattern noise even when background motion may be absent. It also allows for online adaptation to differing intensities of fixed pattern noise. The fixed pattern and shot noise filters are all efficient allowing for real time video processing of PMMW video. We show several examples of PMMW video with complex noise that is much cleaner as a result of the noise removal. Processed video clearly shows cars, houses, trees and utility poles at 20 frames per second.

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T. Nathan Mundhenk, Josh Baron, and Roy M. Matic "Efficient reduction of complex noise in passive millimeter-wavelength video utilizing Bayesian surprise", Proc. SPIE 8042, Display Technologies and Applications for Defense, Security, and Avionics V; and Enhanced and Synthetic Vision 2011, 804210 (1 June 2011); https://doi.org/10.1117/12.887672

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KEYWORDS

Video

Nonuniformity corrections

Image filtering

Passive millimeter wave sensors

Imaging systems

Discrete wavelet transforms

Image processing

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