Jorjandi Sahar, Amini Zahra, Rabbani Hossein
Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
J Med Signals Sens. 2024 Feb 14;14:2. doi: 10.4103/jmss.jmss_58_22. eCollection 2024.
Optical coherence tomography (OCT) imaging has emerged as a promising diagnostic tool, especially in ophthalmology. However, speckle noise and downsampling significantly degrade the quality of OCT images and hinder the development of OCT-assisted diagnostics. In this article, we address the super-resolution (SR) problem of retinal OCT images using a statistical modeling point of view.
In the first step, we utilized Weibull mixture model (WMM) as a comprehensive model to establish the specific features of the intensity distribution of retinal OCT data, such as asymmetry and heavy tailed. To fit the WMM to the low-resolution OCT images, expectation-maximization algorithm is used to estimate the parameters of the model. Then, to reduce the existing noise in the data, a combination of Gaussian transform and spatially constraint Gaussian mixture model is applied. Now, to super-resolve OCT images, the expected patch log-likelihood is used which is a patch-based algorithm with multivariate GMM prior assumption. It restores the high-resolution (HR) images with maximum a posteriori (MAP) estimator.
The proposed method is compared with some well-known super-resolution algorithms visually and numerically. In terms of the mean-to-standard deviation ratio (MSR) and the equivalent number of looks, our method makes a great superiority compared to the other competitors.
The proposed method is simple and does not require any special preprocessing or measurements. The results illustrate that our method not only significantly suppresses the noise but also successfully reconstructs the image, leading to improved visual quality.
光学相干断层扫描（OCT）成像已成为一种很有前景的诊断工具，尤其是在眼科领域。然而，斑点噪声和下采样会显著降低OCT图像的质量，并阻碍OCT辅助诊断的发展。在本文中，我们从统计建模的角度解决视网膜OCT图像的超分辨率（SR）问题。
第一步，我们利用威布尔混合模型（WMM）作为综合模型来建立视网膜OCT数据强度分布的特定特征，如不对称性和重尾性。为了使WMM与低分辨率OCT图像拟合，使用期望最大化算法估计模型参数。然后，为了减少数据中现有的噪声，应用高斯变换和空间约束高斯混合模型相结合的方法。现在，为了对OCT图像进行超分辨率处理，使用期望补丁对数似然法，这是一种基于补丁且具有多元高斯混合模型先验假设的算法。它使用最大后验（MAP）估计器恢复高分辨率（HR）图像。
将所提出的方法与一些著名的超分辨率算法进行了视觉和数值比较。在均值与标准差比（MSR）和等效视数方面，我们的方法比其他竞争对手具有很大优势。
所提出的方法简单，不需要任何特殊的预处理或测量。结果表明，我们的方法不仅能显著抑制噪声，还能成功重建图像，从而提高视觉质量。
