基于先验信息和正则化技术的图像复原算法的研究

Study on image restoration method based on prior information and regularization technique

在湍流退化图像复原研究中,为了消除大气湍流的影响,提出了一种基于先验信息和正则化技术的盲解卷积图像复原算法。该算法是以极大似然估计为基本原理,将目标图像和点扩展函数的先验信息以惩罚项的形式引入到极大似然函数中,同时利用正则化技术优化目标图像和点扩展函数的估计过程,以增加极大似然估计算法的收敛性和稳定性。通过退化图像的复原实验结果表明,该算法在退化模型完全未知的情况下,可以有效的实现对湍流退化图像的盲复原。

A blind deconvolution image restoration algorithm based on prior information and regularization technique is proposed to eliminate the influence of atmospheric turbulence in the turbulence-degraded image restoration method. The basic principle of the algorithm is maximum likelihood theory. It uses the information of object image and point spread function （PSF）, and transforms them into the penalizing function of maximum likelihood. At the same time, the regularization technique is introduced in the course of estimating object image and PSF to enhance the convergence speed and stability of the algorithm. The result of image restoration experiment shows when the model of turbulence-degrade is entirely unknown the algorithm can effectively realize the reconstruction of degraded image.