Neighbor combination for atmospheric turbulence image reconstruction

In this paper, we propose a novel neighbor combination framework for the reconstruction of the atmospheric turbulence degenerated image sequence. To utilize the spatial and temporal redundancy, a neighbor vector sampling strategy in spatial and temporal domain is conducted relying on the modeling of the registered sequence. Then, a combinator of neighbor vectors is developed based on a resampling maximum likelihood model and a relative approximation. Relying on the neighbor combination and spatial-invariant deconvolution, a clear image is reconstructed. Experiments on real data sets demonstrate the effectiveness of this framework.