Simultaneous Turbulence Mitigation and Moving Object Detection

Abstract

AbstractTurbulence mitigation refers to the stabilization of videos with non-uniform deformations due to the influence of optical turbulence. Typical approaches for turbulence mitigation follow averaging or de-warping techniques. Although these methods can reduce the turbulence, they distort the independently moving objects which can often be of great interest. In this chapter, we address the problem of simultaneous turbulence mitigation and moving object detection. We discuss a three-term low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the background, the turbulence, and the object. This extremely difcult problem is simplified into a minimization of nuclear norm, Frobenius norm, and ℓ 1 norm. This method is based on two observations: First, the turbulence causes dense Gaussian noise, and therefore can be captured by Frobenius norm (as the Frobenius norm is equivalent to a squared loss function), while the moving objects are sparse and thus can be captured by ℓ 1 norm. Second, since the objects motion is linear and intrinsically different than the Gaussian-like turbulence, a Gaussian-based turbulence model can be employed to enforce an additional constraint on the search space of the minimization. We demonstrate the performance of the discussed approach on challenging sequences which are signicantly distorted with atmospheric turbulence and include extremely tiny moving objects.KeywordsFrobenius NormRank MinimizationNuclear NormAugmented Lagrange FunctionMove Object DetectionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.