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Image destriping is necessary due to sensor-to-sensor variation within instruments. This has most often been done by assuming that each sensor views a statistically similar subimage, and a histogram of each sensor's response is made to match the overall histogram. Histogram matching shows sensitivity to violations of the similarity assumption. An alternative algorithm is suggested which matches the gain and offset of each sensor to typical values, and which is resistant to the effects of outliers. Tests on a sample image show the moment matching algorithm reduces the variance between sensors to a greater degree than histogram matching.
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Gadallah et al. (2000) studied this question.
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