Least Squares Blind Dcconvolution of Air to Ground Imaging

摘要

Multi-frame iterative blind deconvolution algorithms for image enhancement have been widely used for over ten years. Originally developed for enhancing astronomical images from large ground based telescopes, the algorithms were adapted for ground based satellite observations. Most algorithms involve some type of multi-frame iterative Bayesian optimization assuming either Poisson or Gaussian statistics. Many algorithms use an iterative conjugate gradient search technique, however it has been our experience that an algorithm based on Gaussian statistics, combined with projection onto convex sets adaptation leads to a simple algorithm that quickly converges to a result. Recently our thrust has been to transition these algorithms to the airborne imaging problem. We present a number of examples. First, results from observation of low earth orbit satellites with uncompensated data taken at the focal plane of a large telescope. Finally we move to the problem of air-to-ground imaging. Such scene based imaging scenarios require an algorithm that can operate in the presence of anisoplanatic effects. For this case we have developed an algorithm that calculates a position varying point-spread function.