12 May 2015 Advances in iterative non-uniformity correction techniques for infrared scene projection
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Abstract
Santa Barbara Infrared (SBIR) is continually developing improved methods for non-uniformity correction (NUC) of its Infrared Scene Projectors (IRSPs) as part of its comprehensive efforts to achieve the best possible projector performance. The most recent step forward, Advanced Iterative NUC (AI-NUC), improves upon previous NUC approaches in several ways. The key to NUC performance is achieving the most accurate possible input drive-to-radiance output mapping for each emitter pixel. This requires many highly-accurate radiance measurements of emitter output, as well as sophisticated manipulation of the resulting data set. AI-NUC expands the available radiance data set to include all measurements made of emitter output at any point. In addition, it allows the user to efficiently manage that data for use in the construction of a new NUC table that is generated from an improved fit of the emitter response curve. Not only does this improve the overall NUC by offering more statistics for interpolation than previous approaches, it also simplifies the removal of erroneous data from the set so that it does not propagate into the correction tables. AI-NUC is implemented by SBIR’s IRWindows4 automated test software as part its advanced turnkey IRSP product (the Calibration Radiometry System or CRS), which incorporates all necessary measurement, calibration and NUC table generation capabilities. By employing AI-NUC on the CRS, SBIR has demonstrated the best uniformity results on resistive emitter arrays to date.
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Tom Danielson, Greg Franks, Joe LaVeigne, Marcus Prewarski, Brian Nehring, "Advances in iterative non-uniformity correction techniques for infrared scene projection", Proc. SPIE 9452, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing XXVI, 945210 (12 May 2015); doi: 10.1117/12.2177456; https://doi.org/10.1117/12.2177456
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