Abstract
A novel algorithm is proposed to predict "lucky" regions in a sequence of long-range camera images affected by atmospheric turbulence. Our new approach is to employ bicoherence as a measure of quality to determine lucky regions or good quality image patches from a recorded sequence of anisoplanatic images. The better-quality image regions are selected according to the magnitude of the average value of the bicoherence of each region. Each image patch is restored using bispectral phase estimation from lucky regions, before mosaicing to an overall restoration. Bicoherence can also be used to predict lucky images in the isoplanatic case. Experiments show that our algorithm performs well with both simulated and naturally degraded data.
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