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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