Surveillance imaging from long-range requires use of telescopic optics, and fast electro-optic sensors. The intervening air introduces distortion of the imagery and its spatial frequency content, and does so such that regions of the image suffer dissimilar distortion, visible in the first instance as a time varying geometrical warp, and then as region specific blurring or "speckle". The severity of this, and hence the reduction in size of regions exhibiting similar distortion, is a function of the field of view of the telescope, the height above ground of the imaging path, the range to the target, and climatic conditions.
Image processing algorithms must be run on the sequence of imagery to correct these distortions, on the assumption that exposure time has effectively "frozen" the turbulence. These are absent of knowledge of the actual scene under investigation. Successful algorithms do manage to correct the apparent warping, and in doing so they yield both information on the bulk turbulent medium, and allow for reconstruction of spatial frequency content of the scene that would have been lost by the capability of the optics had their been no turbulence. This is known as turbulence-induced super-resolution.
To confirm the success of algorithms in both correction and reconstruction of such super-resolution we have devised a field experiment where the truth image is known and which uses other methods to evaluate the turbulence for collaboration of the results. We report here a new algorithm, which has proved successful in satellite remote sensing, for restoring this imagery to quality beyond the diffraction limits set by the optics.