Binary weighted averaging of an ensemble of coherently collected image frames

Recent interest in the collection of remote laser radar imagery has motivated novel systems that process temporally contiguous frames of collected imagery to produce an average image that reduces laser speckle, increases image SNR, decreases the deleterious effects of atmospheric distortion, and enhances image detail. This research seeks an algorithm based on Bayesian estimation theory to select those frames from an ensemble that increases spatial resolution compared to simple unweighted averaging of all frames. The resulting binary weighted motion-compensated frame average is compared to the unweighted average using simulated and experimental data collected from a fielded laser vision system. Image resolution is significantly enhanced as quantified by the estimation of the atmospheric seeing parameter through which the average image was formed.