Image deblurring and near-real-time atmospheric seeing estimation through the employment of convergence of variance

Abstract

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. © 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.

Multi‐actuator control with modal switching and different disturbance for scanning imaging motion compensation

“…However, according to the law of reflection, when the mirror rotates, the light path changes. The image of the object moves on the sensor during scanning, leading to image degradation [2,3]. To compensate for the image motion, the sensor or lens motion can be controlled to ensure the stability of the image and the sensor.…”