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 conver- gence 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. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.JRS.7.073504)
Highlights
Both military and civilian applications have driven a significant amount of research on enhancing the quality of images received from optical sensors degraded by the effects of diffraction
In Eq (7), ðx; yÞ are the coordinates for the remote scene, ðu; vÞ are the coordinates in the detector plane, o^ is an estimate for o, and I is the expected value of the intensity received at each detector pixel given a specific point spread function (PSF)
The convergence of variance technique detailed above allows for rapid and accurate estimations of the atmospheric optical transfer function (OTF) parameterized by the seeing parameter, r0
Summary
Both military and civilian applications have driven a significant amount of research on enhancing the quality of images received from optical sensors degraded by the effects of diffraction. This technique is commonly required in situations where the number of photons received from a target is low, as in the case of imaging stellar targets or remote scenes at long slant ranges through the atmosphere. Current military targeting sensors employed in a tactical environment do not account for the effects of atmospheric blur Reasons for this include the fact that blind deconvolution algorithms are often computationally expensive and time intensive. The algorithm was tailored toward sensors employed in a tactical environment where both processing capability and available time are limited For these reasons, we will compare our results to MacDonald’s work as his work represents the only prior attempt to solve the problem of parameterized blind deconvolution to recover atmospheric seeing.
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