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Cumulant-based blur identification a

机译:基于累积量的模糊识别

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Abstract: In this paper, we provide a novel method based on higher order statistic cumulants to identify nonminimum-phase point spread functions and enlarges the possible distribution type of the image formation field. In our method, we specify the blur identification problem as an ARMA model parameter identification problem, but we consider the image model as a realization of a colored signal instead of a normal zero-mean white Gaussian signal, which enlarges the range of image types. For the colored input ARMA model, the contributions of the bicepstrum of ARMA model are only along the axes and 45$DGR degree lines, so we extract the linear parts of cumulant of blur image to analysis, in which we use higher-order statistic techniques to estimate the ARMA parameters. The experiments are present in this paper.!15
机译:摘要:本文提供了一种基于高阶统计累积量的新方法,用于识别非最小相位点扩散函数,并扩大了成像场的可能分布类型。在我们的方法中,我们将模糊识别问题指定为ARMA模型参数识别问题,但我们将图像模型视为彩色信号的实现,而不是正常的零均值白高斯信号,这扩大了图像类型的范围。对于彩色输入的ARMA模型,ARMA模型的二头肌的贡献仅沿轴和45 $ DGR度线,因此我们提取模糊图像累积量的线性部分进行分析,其中我们使用高阶统计技术估计ARMA参数。实验已在本文中提出!15

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