Image Reconstruction from Videos Distorted by Atmospheric Turbulence

To correct geometric distortion and reduce blur in videos that suffer from atmospheric turbulence, a multi-frame image reconstruction approach is proposed in this paper. This approach contains two major steps. In the first step, a B-spline based non-rigid image registration algorithm is employed to register each observed frame with respect to a reference image. To improve the registration accuracy, a symmetry constraint is introduced, which penalizes inconsistency between the forward and backward deformation parameters during the estimation process. A fast Gauss-Newton implementation method is also developed to reduce the computational cost of the registration algorithm. In the second step, a high quality image is restored from the registered observed frames under a Bayesian reconstruction framework, where we use L-1 norm minimization and a bilateral total variation (BTV) regularization prior, to make the algorithm more robust to noise and estimation error. Experiments show that the proposed approach can effectively reduce the influence of atmospheric turbulence even for noisy videos with relatively long exposure time.