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

Blind Deconvolution Method of Image Deblurring Using Convergence of Variance

Images are used for both aerial and space imagery applications, including target detection and tracking. The current problem concerning objects in geosynchronous orbit is that they are dim and hard to resolve because of their distance. This work will further the combined effort of AFIT and AFRL to provide enhanced space situational awareness (SSA) and space surveillance. SSA is critical in a time when many countries possess the technology to put satellites into orbit. Enhanced imaging technology improves the Air Force\u27s ability to see if foreign satellites or other space hardware are operating in the vicinity of our own assets at geosynchronous orbit. Image deblurring or denoising is a crucial part of restoring images that have been distorted either by movement during the capture process, using out-of-focus optics, or atmospheric turbulence. The goal of this work is to develop a new blind deconvolution method for imaging objects at geosynchronous orbit. It will feature an expectation maximization (EM) approach to iteratively deblur an image while using the convergence of the image\u27s variance as the stopping criteria