稀疏梯度先验模型的正则化图像复原

Regularized image restoration algorithm on sparse gradient prior model

传统Lucy-Richardson(LR)算法是一种基于贝叶斯分析的图像复原迭代算法,对高信噪比的退化图像能获得很好的复原结果,但对噪声过于敏感,对低信噪比的退化图像在迭代过程中易造成噪声的放大,虽然有一些正则化方法应用到LR算法中来抑制噪声,但往往容易产生过度平滑的问题。针对这些问题将图像稀疏先验模型作为正则项引入到LR算法中,抑制噪声在迭代过程中的放大。与常规的图像梯度约束算法不同,本文算法中根据模糊图像梯度分布特点的不同提出了可变参数的图像稀疏梯度正则化约束方法,使复原图像的梯度分布参数在迭代过程中更趋近于真实梯度分布,同时通过调整正则项系数可以避免复原图像的过度平滑。实验结果表明,同标准LR算法和常规梯度约束算法相比,本文算法能够实现在抑制噪声放大的同时较好地保留图像的细节。

The traditional Lucy-Richardson algorithm is an iterative image restoration method based on Bayesian analysis. It achieves good results for restoring images degraded with a high signal-to-noise ratio （SNR）. The algorithm is so sensitive to noises that some regularized methods are introduced into the LR-algorithm. However, these tricks often tend to produce excessive smoothing. Therefore, in this paper, we introduce the image sparse prior model as a regularization item into the LR-algorithm, and get a new regularization LR algorithm to suppress noise amplification in the iterative process. To be dif- ferent from the conventional gratitude-restriction approaches, the algorithm proposes a varying parameterized sparse gradient regularization restriction method, which enables the gradient distribution parameters of the restored image more close to the true gradient distribution and avQids excessive smoothing of restored image by adjusting the regular coefficient. The experi- mental results show that the algorithm can efficiently suppress the amplification of noises and preserve the details of images.