摘要
用基于深度变化成像模型的调整EM算法进行三维显微图像复原,不能更好地复原图像细节,而且耗时长。为提高图像的复原质量,缩短时间,提出把维纳滤波和调整EM算法相结合的算法。该算法首先利用加权小波去除图像的部分离焦模糊,再用维纳滤波算法进行滤波复原,最后用基于深度变化成像模型的调整EM算法对序列图进行复原。实验表明复原效果得到了明显改善,并减少了迭代次数,效率明显提高。
In the image restoration of Computational Optical Sectioning Microscopy（COSM）,using regularized Expectation Maximization（EM） algorithm based on the depth-variant imaging model can not recover the more detail of image, and consume more time.In order to improve the restored results and reduce the time of the image restoration, the combination of the regularized EM algorithm and Wiener filter algorithm is proposed.Firstly, the weighted wavelet algorithm is used to reduce the information out of focus.Then,the Wiener filter algorithm and the regularized EM algorithm is used to restore the serial images in turn.Experiments show that the restoration result is improved and the iteration time is reduced.The efficiency is increased obviously.
出处
《计算机工程与应用》
CSCD
北大核心
2011年第15期180-183,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.61071161~~
关键词
图像复原
加权小波
维纳滤波
调整EM算法
计算光学切片显微成像
image restoration
weighted wavelet
Wiener filter
regularized EM algorithm
computational optical sectioning microscopy
