利用加权预测的图像迭代盲解卷积  

Iterative blind deconvolution of image with weighted prediction

作　　者：邸男[1] 付东辉[1] 王毅楠[2] 

机构地区：[1]中国科学院长春光学精密机械与物理研究所,吉林长春130033 [2]长春轨道客车股份有限公司,吉林长春130062

出　　处：《中国光学》2011年第5期514-518,共5页Chinese Optics

基　　金：国家863高技术研究发展计划资助项目(No.2006AA703405F)

摘　　要：为解决大气湍流造成的图像退化问题,本文鉴于现有的盲解卷积算法收敛性不稳定,计算量大等特点,提出了一种基于加权预测的迭代盲解卷积算法。对目前性能优秀的用迭代实现盲解卷积的L-R算法进行优化,在每次迭代结束后通过加权方法求出预测值,根据预测值计算方向加速算子,从而大大提高算法的收敛速度。实验表明:该算法不仅可对模糊退化图像进行很好的复原,同时与L-R算法相比收敛速度提高约43.8倍,其迭代速度快的特点决定了算法具有较高的工程实用价值。To modify the image degradation caused by atmospheric turbulence,this paper proposes a new algorithm based on the iterative blind deconvolution with weighted prediction to solve the problems on instability convergence and huge complexity from traditional blind deconvolution methods.By optimizing an existing iterative blind deconvolution L-R algorithm,the proposed algorithm uses the weighting to obtain prediced values at the end of every iterative step,it then calculates the acceleration operators according to the predicted values to improve its convergence speed.Experiments show that the algorithm is capable of restoring the turbulence degraded image and the convergence have speeded about 43.8 times as compared with that of L-R algorithm.The algorithm′s fast convergence shows its great practical value.

关 键 词：迭代盲解卷积 加权预测 L-R算法 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]