基于EM算法的眼底OCT图像反卷积去模糊技术
Deblurring Techniques Combined with Deconvolution of OCT Retinal Image Based on Expectation Maximization

光学相干层析成像技术（Optical coherence tomography，OCT）在视网膜检查中十分重要，然而在获取OCT图像时眼球运动或者散焦作用都可能引起图像的模糊，从而为临床诊断造成困难。因此，从模糊OCT图像中恢复出清晰图像的去模糊技术研究至关重要。本文结合OCT成像原理，提出了一种基于最大期望（Expectation-maximization，EM）算法的OCT图像反卷积技术。该技术能够在一定程度上抑制OCT模糊图像中异常值对复原图像的干扰，从而有效去除OCT图像中的模糊。将本文技术与多种现有广义图像去模糊技术进行了实验比较，结果表明本文提出的复原OCT图像的反卷积算法在眼底OCT图像去模糊的细节恢复方面效果较好。
Optical coherence tomography (OCT) plays a very important role in retinal/choroidal examination. However, image blurring can be introduced into the OCT images during the image acquirement process, resulted from the movement of eye or the out-of-focus effect of the imaging machine. Therefore, OCT image deblurring is of great importance in practice. In this paper, we use an expectation-maximization (EM) based image deconvolution technique for eliminating the blur effect in the OCT images. The proposed technique is designed based on several of the special characteristics of the OCT retinal image and is validated to be able to outperform various state-of-the-art image deblurring techniques as shown by the experimental results in the paper.