Priority and significance analysis of selecting threshold values in Adaptive Unsharp Masking for infrared images

Infrared image processing has been the focal point of considerable research activity in the last decade mainly because of its wide application areas in security and defense. With the aid of an existing image enhancement technique, Adaptive Unsharp Masking (AUM), proposed optimum parameters selection procedure delivers better performance in sharpness and contrast adjustment for the detection of targets in interest in objective quality metrics. In our study, we focus on the lower and upper limit threshold parameters which classify the contrast areas and therefore have significant effect on sharpening the edges for two different infrared (IR) images. The experimental results prove that changes of convergence rate of adaptive filter and positive convergence parameter versus mean square error (MSE) exhibit similar characteristics under certain lower and upper threshold values which satisfy “minimum” MSE; that is these threshold values are the most dominant criterion amongst the other parameters and they are significant.