Joint regularization and low-rank fusion for atmospheric turbulence removal

Wenjin Yang

Abstract:

Atmospheric turbulence removal remains a challenging task, because it is very difficult to mitigate geometric distortion and
remove spatially and temporally variant blur. This paper presents a novel strategy for atmospheric turbulence removal by
characterizing local smoothness, nonlocal similarity and low-rank property of natural images. The main contributions are
three folds. First, a joint regularization model is made which combines nonlocal total variation regularization and steering
kernel regression total variation regularization in order that reference image enhancement and image registration are jointly
implemented on geometric distortion reduction. Secondly, a fast split Bregman iteration algorithm is designed to address
the joint variation optimization problem. Finally, a weighted nuclear norm is introduced to constrain the low-rank optimization problem to reduce blur variation and generate a fusion image. Extensive experimental results show that our
method can effectively mitigate geometric deformation as well as blur variations and that it outperforms several other stateof-the-art turbulence removal methods.