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Journal of Applied Remote Sensing • Open Access

Image deblurring and near-real-time atmospheric seeing estimation through the employment of convergence of variance
Author(s): Brian J. Neff; Quentin D. MacManus; Stephen C. Cain; Richard K. Martin

Paper Abstract

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.

Paper Details

Date Published: 20 September 2013
PDF: 26 pages
J. Appl. Remote Sens. 7(1) 073504 doi: 10.1117/1.JRS.7.073504
Published in: Journal of Applied Remote Sensing Volume 7, Issue 1
Show Author Affiliations
Brian J. Neff, Air Force Institute of Technology (United States)
Quentin D. MacManus, Air Force Institute of Technology (United States)
Stephen C. Cain, Air Force Institute of Technology (United States)
Richard K. Martin, Air Force Institute of Technology (United States)


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