Comparing horizontal path C<sup>2</sup>n measurements over 0.6 km in the tropical littoral environment and in the desert

Chang, Mark P. J. L.; Font, Carlos O.; Gilbreath, G. C.; Oh, Eunha; Distefano, Emi; Restaino, Sergio R.; Wilcox, Christopher C.; Santiago, Freddie

doi:10.1117/12.718257

Cited by 4 publications

(3 citation statements)

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“…Exact modelling of the turbulence is in fact ill-posed as it follows a non-uniform distribution which varies significantly in time, besides having an additional complexity introduced during the imaging process; thus, rendering the problem of modelling turbulence extremely difficult. Although the refraction index of the turbulent medium is often randomly changing, it is also statistically stationary [3], [30], [36]; thus, the deformations caused by turbulence are generally repetitive and locally centered [6], [7], [8], [9], [37]; this encourages the use of Gaussian-based models as approximate distributions that are general enough to avoid overfitting, but rather capture significant portion of the turbulent characteristics.…”

Section: Turbulence Modelmentioning

confidence: 99%

“…(x t w , y t h )) to model the turbulence motion in the scene. The locations visited by a particle moving due to the fluctuations of the turbulence have a unimodal and symmetric distribution which approaches a Gaussian [6], [30], [10], [9]. This is dissimilar from the linear motion of the particles driven by moving objects.…”

Section: Turbulence Modelmentioning

confidence: 99%

“…2) The Turbulence: Previous work dealing with turbulence, such as [6], [7], [8], [9], [10], demonstrated that the fluctuations of fluids (for instance air and water) attain Gaussian-like characteristics such as being unimodal, symmetric, and locally repetitive; therefore, the projected deformations in the captured sequence often approach a Gaussian distribution (we discuss this in more detail in Section 3.2). For this reason, the turbulence component can be captured by minimizing its Frobenius norm.…”

Section: Introductionmentioning

confidence: 99%

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Simultaneous Video Stabilization and Moving Object Detection in Turbulence

Oreifej

Shah

2013

IEEE Trans. Pattern Anal. Mach. Intell.

209

168

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Abstract-Turbulence 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 paper, we address the novel problem of simultaneous turbulence mitigation and moving object detection. We propose a novel threeterm low-rank matrix decomposition approach in which we decompose the turbulence sequence into three components: the background, the turbulence, and the object. We simplify this extremely difficult problem into a minimization of nuclear norm, Frobenius norm, and 1 norm. Our method is based on two observations: First, the turbulence causes dense and Gaussian noise, and therefore can be captured by Frobenius norm, while the moving objects are sparse and thus can be captured by 1 norm. Second, since the object's 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 robustness of our approach on challenging sequences which are significantly distorted with atmospheric turbulence and include extremely tiny moving objects.

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