  We address the problem of restoring a high-quality image from an observed
image sequence strongly distorted by atmospheric turbulence. A novel algorithm
is proposed in this paper to reduce geometric distortion as well as
space-and-time-varying blur due to strong turbulence. By considering a suitable
energy functional, our algorithm first obtains a sharp reference image and a
subsampled image sequence containing sharp and mildly distorted image frames
with respect to the reference image. The subsampled image sequence is then
stabilized by applying the Robust Principal Component Analysis (RPCA) on the
deformation fields between image frames and warping the image frames by a
quasiconformal map associated with the low-rank part of the deformation matrix.
After image frames are registered to the reference image, the low-rank part of
them are deblurred via a blind deconvolution, and the deblurred frames are then
fused with the enhanced sparse part. Experiments have been carried out on both
synthetic and real turbulence-distorted video. Results demonstrate that our
method is effective in alleviating distortions and blur, restoring image
details and enhancing visual quality.
