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Compensating image degradation due to atmospheric turbulence in anisoplanatic conditions

In imaging applications the prevalent effects of atmospheric turbulence comprise image dancing and image blurring. Suggestions from the field of image processing to compensate for these turbulence effects and restore degraded imagery include Motion-Compensated Averaging (MCA) for image sequences. In isoplanatic conditions, such an averaged image can be considered as a non-distorted image that has been blurred by an unknown Point Spread Function (PSF) of the same size as the pixel motions due to the turbulence and a blind deconvolution algorithm can be employed for the final image restoration. However, when imaging over a long horizontal path close to the ground, conditions are likely to be anisoplanatic and image dancing will effect local image displacements between consecutive frames rather than global shifts only. Therefore, in this paper, a locally operating variant of the MCA-procedure is proposed, utilizing Block Matching (BM) in order to identify and re-arrange uniformly displaced image parts. For the final restoration a multistage blind deconvolution algorithm is used and the corresponding deconvolution results are presented and evaluated.