We showed that a hard threshold for wavelet denoising based on higher order statistics is comparable to a second
order soft threshold. The hard threshold can made adaptive by using a third order statistic as an estimate of the
noise. In addition, the relationship between an adaptive hard threshold and retaining a fraction of wavelet
coefficients is shown. Qualitative and quantitative metrics based on the mean-squared error are used to compare the
hard thresholding and a soft-thresholding technique, BayesShrink.
Hard thresholding seems to work well for denoising signals using higher-order statistics. We statistically examined
the best values for hard thresholding and related this to the fraction of wavelet coefficients set to zero to obtain the
minimum MSE. In addition, we found that the minimum MSE obtained was less sensitive to the threshold when
implemented based on a third-order parameter rather than the noise power. Alternatively, we found that this
approach to thresholding could be implemented by setting a fixed fraction of wavelet coefficients to zero.
Proc. SPIE. 2224, Infrared Imaging Systems: Design, Analysis, Modeling, and Testing V
KEYWORDS: Imaging systems, Sensors, Interference (communication), 3D metrology, Transmittance, Modulation transfer functions, Analog electronics, Forward looking infrared, Minimum resolvable temperature difference, Temperature metrology
This paper presents the measurement requirements and algorithms that characterize forward looking IR (FLIR) imaging systems in the Martin Marietta EO Characterization Lab. The algorithms presented automate the following major measurement requirements: signal transfer functions, 3D noise, noise equivalent temperature difference, fixed pattern noise, nonuniformity, modulation transfer function, and minimum resolvable temperature difference.