基于最小二乘准则的模糊估计和图像复原  

Blur Identification and 3D Image Restoration Based on Least-square Theory

作　　者：卿粼波[1] 何小海[1] 陶青川[1] 吕成淮[1] 张菊[1] 孙贵凡[1] 

机构地区：[1]四川大学电子信息学院,图像信息研究所,四川成都610064

出　　处：《四川大学学报（工程科学版）》2008年第2期129-133,共5页Journal of Sichuan University (Engineering Science Edition)

基　　金：国家自然科学基金资助项目(60372079)

摘　　要：在计算光学显微成像技术中,点扩展函数往往是未知的,且不易获取,从而给图像复原带来很大困难。基于最小二乘准则和最优化理论,提出了利用变尺度法的三维点扩展函数参数估计算法;针对传统EM算法存在复原效果细节丢失严重等问题,提出最小二乘共轭梯度三维图像复原算法。算法在点扩展函数参数估计和求解真实图像之间进行交替迭代,从而得到图像的最优估计。实验表明,新算法在较短时间内,能够较准确地估计出点扩展函数参数,并得到较好的复原结果。The point spread function（ PSF） in computational optical sectioning microscopy （COSM） technology was always unknown and hard to obtain. This brought a lot of difficulties to image restoration. Based on least-square and optimal theory, a parameter estimation method using variable metric was proposed for 3D point spread function. In addition, to overcome the traditional EM algorithm＇ s limit such as serious detail loss, a conjugate gradient leastsquare（CGLS） algorithm for the restoration of 3D images was developed. The optimal real image estimation was obtained through alterative iteration between the parameter estimation of point spread function and the real images estimation. Experimental results showed that the new algorithm could successfully identify blurs and restore images, in a short time.

关 键 词：计算光学显微成像 最小二乘 模糊估计 图像复原 

分 类 号：TP751[自动化与计算机技术—检测技术与自动化装置]