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Image restoration with an a-priori estimation of the point spread function

机译:图像恢复具有Point扩展功能的a-priori估计

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An approach to the deconvolution of images of the Earth's underlying surface considering the distorting effect of the atmosphere is considered. The peculiarity of this approach is that the point spread function (PSF) used in the linear model of image restoration is unknown and should be estimated a priori. To this end the same image is used and information that the observable scene comprises objects with contrast brightness gradients. The Gumbel distribution of extremal values is taken as a stochastic model of image fragments with high gradients. For the alternative the family of the Johnson curves has been chosen. The Bayes decision rule based on these distributions identifies the external gradients. The brightness variations of a blurred image fragment along the gradients serve as a material for the PSF restoration. The image itself is restored with the use of a standard approach. An example is given to illustrate the PSF and blurred image restoration.
机译:考虑了地球底面图像图像的成卷积的方法,考虑到大气的扭曲效应。这种方法的特殊性是图像恢复线性模型中使用的点扩展功能(PSF)是未知的,并且应该估计先验。为此,使用相同的图像和可观察场景包括具有对比度亮度梯度的对象的信息。极值值的Gumbel分布作为具有高梯度的图像片段的随机模型。对于替代方案,选择了约翰逊曲线的家庭。基于这些分布的贝叶斯决策规则标识外部梯度。沿梯度模糊的图像片段的亮度变化用作PSF恢复的材料。使用标准方法恢复图像本身。给出一个例子来说明PSF和模糊图像恢复。

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