  It remains a challenge to simultaneously remove geometric distortion and
space-time-varying blur in frames captured through a turbulent atmospheric
medium. To solve, or at least reduce these effects, we propose a new scheme to
recover a latent image from observed frames by integrating a new variational
model and distortion-driven spatial-temporal kernel regression. The proposed
scheme first constructs a high-quality reference image from the observed frames
using low-rank decomposition. Then, to generate an improved registered
sequence, the reference image is iteratively optimized using a variational
model containing a new spatial-temporal regularization. The proposed fast
algorithm efficiently solves this model without the use of partial differential
equations (PDEs). Next, to reduce blur variation, distortion-driven
spatial-temporal kernel regression is carried out to fuse the registered
sequence into one image by introducing the concept of the near-stationary
patch. Applying a blind deconvolution algorithm to the fused image produces the
final output. Extensive experimental testing shows, both qualitatively and
quantitatively, that the proposed method can effectively alleviate distortion
and blur and recover details of the original scene compared to state-of-the-art
methods.

Computer Vision and Pattern Recognition