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。

Based on the assumption of a generalized Gaussian model for the additive noise, this paper develops the regularized image restoration algorithm and proposes anlp-norm data item to the regularization objective functionnal instead of the usual quadratic data item. Meanwhile, the paper applies an adaptive method for choosing the regularization parameter. An iterative algorithm is utilized for obtaining the restored image and the regularization parameter, which can be determined in terms of the partly restored image at each iteration step therefore allowing for the simultaneous determination of the restoration of the degraded image and the value of the regularization parameter. Numerical experiments demonstrate that our method results in high restoration performance when the image was blurred by a Gaussian PSF and an additive generalized Gaussian noise, especially when the shape parameter p≤1.