首页> 外文期刊>Mathematical Problems in Engineering >Deconvolution of Defocused Image with Multivariate Local Polynomial Regression and Iterative Wiener Filtering in DWT Domain
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Deconvolution of Defocused Image with Multivariate Local Polynomial Regression and Iterative Wiener Filtering in DWT Domain

机译:DWT域中具有多元局部多项式回归和迭代维纳滤波的离焦图像解卷积

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摘要

A novel semiblind defocused image deconvolution technique is proposed, which is based on multivariate local polynomial regression (MLPR) and iterative Wiener filtering (IWF). In this technique, firstly a multivariate local polynomial regression model is trained in wavelet domain to estimate defocus parameter. After obtaining the point spread function (PSF) parameter, iterative wiener filter is adopted to complete the restoration. We experimentally illustrate its performance on simulated data and real blurred image. Results show that the proposed PSF parameter estimation technique and the image restoration method are effective.
机译:提出了一种基于多元局部多项式回归(MLPR)和迭代维纳滤波(IWF)的半盲散焦图像去卷积技术。在该技术中,首先在小波域中训练多元局部多项式回归模型以估计散焦参数。获得点扩展函数(PSF)参数后,采用迭代维纳滤波器完成恢复。我们通过实验来说明其在模拟数据和真实模糊图像上的性能。结果表明,提出的PSF参数估计技术和图像复原方法是有效的。

著录项

  • 来源
    《Mathematical Problems in Engineering》 |2010年第2期|p.44.1-44.14|共14页
  • 作者

    Liyun Su; Fenglan Li;

  • 作者单位

    School of Mathematics and Statistics, Chongqing University of Technology, Chongqing 400054, China;

    Library, Chongqing University of Technology, Chongqing 400054, China;

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  • 正文语种 eng
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