基于深度变化成像模型的图像估计

Image Estimation Based on Depth-variant Imaging Model

该文提出了一种基于EM算法的最大似然图像复原算法,此算法是基于三维显微光学切片中成像随深度变化的模型实现的。实际成像中,由于样本中物质是变化的,故样本不同位置的折射率不一样,并且会导致其点扩展函数也不同。虽然大多数显微镜具有像差补偿功能,但由于样本的折射率和物镜的折射率不匹配,导致不同深度其点扩展函数也不一样。该文对二维图像和三维图像序列进行实验,结果表明通过此算法能够补偿由于深度变化所带来的模糊,从而将模糊图像复原。

In the paper an algorithm for maximum-likelihood image restoration based on the expectation maximization(EM) algorithm using the depth-variant imaging model in three-dimensional optical sectioning microscopy is proposed.Because the substance of specimen is various,the refractive index in different depth is different and the PSF is different.Although most microscope objectives have the ability to eliminate the spherical aberration,whereas the refractive index mismatch between the specimen and the objectives,the PSF is depth variant.This paper analyzes the capability of the EM algorithm with two-dimension images restoration and three-dimension serial images restoration,and its performance shows that the algorithm can compensate the blur of image by the depth variant image model.