Image Deblurring and Near-real-time Atmospheric Seeing Estimation through the Employment of Convergence of Variance
A new image reconstruction algorithm is presented that will remove the effect of atmospheric turbulence on motion compensated frame average images. The primary focus of this research was to develop a blind deconvolution technique that could be employed in a tactical military environment where both time and computational power are limited. Additionally, this technique can be employed to measure atmospheric seeing conditions. In a blind deconvolution fashion, the algorithm simultaneously computes a high resolution image and an average model for the atmospheric blur parameterized by Fried’s seeing parameter. The difference in this approach is that it does not assume a prior distribution for the seeing parameter, rather it assesses the convergence of the image’s variance as the stopping criteria and identification of the proper seeing parameter from a range of candidate values. Experimental results show that the convergence of variance technique allows for estimation of the seeing parameter accurate to within 0.5 cm and often even better depending on the signal to noise ratio.
Journal of Applied Remote Sensing
Neff, B. J., MacManus, Q. D., Cain, S. C., & Martin, R. K. (2013). Image deblurring and near-real-time atmospheric seeing estimation through the employment of convergence of variance. Journal of Applied Remote Sensing, 7, 73504. https://doi.org/10.1117/1.JRS.7.073504
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