The natural image prior generalizes the heavy-tailed gradient distributions of clear images to $L_p$ regularized problems in the image deconvolution process. Employing a maximum a posteriori estimator, this prior should be carefully selected to precisely model the gradient statistics of the corresponding natural image. However, in several deconvolution algorithms, $p$ has been randomly determined to obtain a high-quality image without considering the essence of the image prior. In this study, we proposed an automatic prior selection strategy based on the statistical properties of restored images. The probabilistic characteristics of the images were derived and investigated by statistically modeling the individual gradient distributions. Subsequently, the regularization term of the objective function was iteratively updated based on the analysis of image restoration. Instead of the unavailable original images, we focused on the utilization of the observed image to estimate the image prior. Overcoming the ill-posedness of the prior selection problem, the proposed algorithm achieved the optimal image prior and effectively restored the degraded image simultaneously.

自然图像先验将清晰图像的重尾梯度分布推广到 ${升}_{磷}$图像去卷积过程中的正则化问题。使用最大后验估计器，应该仔细选择这个先验以精确建模相应自然图像的梯度统计。然而，在几种反卷积算法中，$磷$已经随机确定以获得高质量图像，而不考虑先验图像的本质。在这项研究中，我们提出了一种基于恢复图像的统计特性的自动先验选择策略。通过对各个梯度分布进行统计建模，可以导出和研究图像的概率特征。随后，基于图像恢复分析，迭代更新目标函数的正则化项。代替不可用的原始图像，我们专注于利用观察到的图像来估计图像先验。该算法克服了先验选择问题的不适定性，实现了最优图像先验，同时有效地恢复了退化图像。