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基于广义高斯噪声分布模型的迭代正则化图像复原

DOI: 10.11834/jig.200408187

Keywords: 图像复原,广义高斯分布模型,正则化,高斯噪声,形状参数

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Abstract:

讨论了广义高斯分布加性噪声模型,从对图像的最大似然估计出发,结合正则化的复原方法,提出了具有lp范数数据逼近项的正则化目标泛函,同时给出了自适应的正则化参数选择方法。对目标泛函使用迭代的方法求解,分析了迭代式的收敛性。目标泛函中正则化参数的选择和图像复原的迭代运算同步进行并自动优化。实验结果表明,对于加有广义高斯分布噪声的并被高斯型点扩展函数模糊的图像,该方法可明显改善图像复原的效果,尤其当广义高斯分布的形状参数p≤1时复原效果更好。

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