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Image Reconstruction from Videos Distorted by Atmospheric Turbulence

机译:从大气湍流扭曲的视频图像重建

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To correct geometric distortion and reduce blur in videos that suffer from atmospheric turbulence, a multi-frame image reconstruction approach is proposed in this paper. This approach contains two major steps. In the first step, a B-spline based non-rigid image registration algorithm is employed to register each observed frame with respect to a reference image. To improve the registration accuracy, a symmetry constraint is introduced,which penalizes inconsistency between the forward and backward deformation parameters during the estimation process. A fast Gauss-Newton implementation method is also developed to reduce the computational cost of the registration algorithm. In the second step, a high quality image is restored from the registered observed frames under a Bayesian reconstruction framework, where we use L_1 norm minimization and a bilateral total variation (BTV) regularization prior, to make the algorithm more robust to noise and estimation error. Experiments show that the proposed approach can effectively reduce the influence of atmospheric turbulence even for noisy videos with relatively long exposure time.
机译:为了纠正遭受大气湍流的视频的几何失真和减少模糊,本文提出了一种多帧图像重建方法。这种方法包含两个主要步骤。在第一步中,采用基于B样条曲线的非刚性图像配准算法来相对于参考图像向每个观察到的帧寄存。为了提高登记准确性,引入了对称约束,在估计过程中惩罚前向和后向变形参数之间的不一致。还开发了一种快速的高斯 - 牛顿实现方法来降低登记算法的计算成本。在第二步中,从贝叶斯重建框架下的注册观察框架恢复了高质量的图像,在其中我们使用L_1 Norm最小化和双边总变化(BTV)正则化,使算法对噪声和估计误差更加强大。实验表明,所提出的方法可以有效地降低大气湍流的影响,即使是具有相对较长的曝光时间的嘈杂视频。

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