8 October 2007 Implementation for temporal noise identification using adaptive threshold of infrared imaging system
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Abstract
Bad pixels are spatial or temporal noise which arise from dead pixels by fixed signal levels or blinking pixels by variable signal levels that go beyond the bounds of normal pixel levels at the temperature. Because bad pixels are the false targets over infrared imaging system for tracking, those must be corrected. Main contribution to the number of bad pixels is fixed pattern noise (FPN) according to increasing array size. And it is more simple to establish whether FPN is or not through analyzing of accumulated frames. But it needs to calculate with more complex implementation such standard deviation from frame to frame in case of the temporal noise. Both cases it is very important to establish the threshold levels for identifying at variable operating temperatures. In this paper, we propose a more efficient data analysis method and a temporal noise identification method using adaptive threshold for infrared imaging system, and the hardware is implemented to identify and replace bad pixels. And its result is confirmed visually by bad pixel map images.
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Inok Lim, "Implementation for temporal noise identification using adaptive threshold of infrared imaging system", Proc. SPIE 6737, Electro-Optical and Infrared Systems: Technology and Applications IV, 673718 (8 October 2007); doi: 10.1117/12.738681; https://doi.org/10.1117/12.738681
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