基于深度变化成像模型的调整EM算法

在实际成像中,通常样本中的物质是变化的,故样本中不同位置的折射率不一样。由于三维样本的折射率与物镜所浸物质的折射率的不匹配,导致不同深度的点扩展函数可能不同。在此深度变化成像模型基础上应用最大期望(EM)复原算法能够提高图像清晰度,尤其是深度方向,但会丢失图像的一些微弱细节且出现一些孤立亮点,因此将调整EM算法运用到基于三维显微光学切片中成像随深度变化的图像模型上,此二者结合后的新算法可以避免上述缺点,较好地恢复图像微弱细节。

Regularized EM image estimation based on depth-variant imaging model

In practical imaging,because the substance of a specimen varies spatially,the refractive indexes in different depth are different.A large mismatch of the refractive index of 3D specimen and immersion medium leads to different PSFs in different depths.Using Expectation Maximization(EM) algorithm based on the depth-variant imaging model can improve image resolution,especially in depth,but it would result in loosing dim detail and enhancing very bright isolated spots.A regularized EM algorithm was used to avoid disadvantages and recover the detail of image in the depth-variant imaging model in three-dimensional optical sectioning microscopy.