一种空间自适应正则化图象盲复原算法

A Space-Adaptive Regularization Approach for Blind Image Restoration

图象盲复原所面临的主要问题是可利用信息的不足 ,所以必须充分利用图象本身及成像系统的先验信息 .为此 ,结合模糊先验辨识的思想 ,给出了一种新的空间自适应正则化算法 ,该算法先用交替最小化的迭代方法对模糊进行先验辨识 ,然后利用辨识结果 ,用各向异性扩散进行图象复原 .算法充分利用了图象及成像系统 (或点扩散函数 PSF)的分段平滑特性 ,同时又利用各向异性扩散的概念 ,使得正则化不仅在程度上 ,而且在方向上都是空间自适应的 ,从而能够有效地进行图象盲复原 .仿真结果表明 ,该算法的复原效果优于空间自适应各向同性正则化 (SAR)算法 ,其收敛性能优于空间自适应各向异性正则化 (SAAR)算法 .

A new space-adaptive regularization method for blind image restoration, which combines the idea of priori blur identification is presented. This new technique first identifies the point spread function(PSF) by using alternating minimization iterative algorithm. Then it restores the image based on the identified PSF using anisotropic regularization. The main difficulty in blind image restoration is insufficient information, which demands full utilization of priori knowledge of image itself and imaging system. This algorithm utilizes the piecewise smoothness of both the image and the PSF, and it simultaneously makes use of the concept of anisotropic diffusion, which carries out space adaptive regularization according to the orientations of the image and the PSF. This new method's efficiency is demonstrated by numerically blurred images. It can get better restoration images than space adaptive regularization(SAR) method, and converge faster than space adaptive anisotropic regularization(SAAR) method