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30 April 2007 Designing an error metric for super-resolution enhanced IR passive ranging
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Assessment of image resolution enhancement on range estimation using stereo vision systems provides valuable insight to the design and feasibility of advanced passive ranging systems. Application of such enhancements to stereo analysis for visible-band cameras has shown promising results in the past. These methods need to be extended to the infrared band for a day/night operational capability and, in particular, the performance of uncooled infrared sensors needs to be quantified. Here how resolution enhancement affects the estimation of "stereo disparity", a quantity that directly relates to range, is examined empirically using a low-resolution uncooled staring infrared camera, and the results are analyzed with respect to measured data. Currently available resolution enhancement algorithms such as those based on Maximum A Posteriori (MAP) and Markov Chain Monte Carlo (MCMC) methods are utilized. The variance of disparity estimation error is chosen as a metric for performance, and is examined as a function of algorithm parameters, target-to-background differential temperature, image noise, and baseline distance. Based on the metric, an empirical model for performance gain is introduced. Overall, resolution enhancement processing is beneficial to stereo disparity estimation especially when signal-to-noise ratio is high, and when sample-scene phasing impedes the accuracy of estimation.
© (2007) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jae H. Cha and A. Lynn Abbott "Designing an error metric for super-resolution enhanced IR passive ranging", Proc. SPIE 6543, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XVIII, 65430Y (30 April 2007);

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