Effective Speckle reduction and structure enhancement method for retinal OCT image based on VID and Retinex

Biyuan Li, Yu Wang, Jun Zhang
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Abstract

Improving the quality of images is one of the key tasks in Optical Coherence Tomography (OCT) imaging technology. Low contrast and speckle noise are two major factors affecting the accuracy of OCT measurement. In this paper, an effective speckle reduction and structure enhancement method is proposed based on variational image decomposition (VID) and multi-scale Retinex (MSR). To be specific, we propose a new variational image decomposition model BL-G-BM3D to decompose the OCT image into background part, structure part and noise. Then the structure part is enhanced by MSR and the background part is used to generate a filter mask by fuzzy c-means clustering algorithm. Experimental results show that the proposed method performs well in speckle reduction and structure enhancement, with better quality metrics of the SNR, CNR, and ENL and better fine detail retention than shearlet transform method and BM3D method.
基于VID和Retinex的视网膜OCT图像有效斑点消减和结构增强方法
提高图像质量是光学相干层析成像技术的关键问题之一。低对比度和散斑噪声是影响OCT测量精度的两个主要因素。本文提出了一种基于变分图像分解(VID)和多尺度Retinex (MSR)的有效斑点减少和结构增强方法。本文提出了一种新的变分图像分解模型BL-G-BM3D,将OCT图像分解为背景部分、结构部分和噪声部分。然后对结构部分进行MSR增强,背景部分采用模糊c均值聚类算法生成滤波掩模。实验结果表明,与shearlet变换方法和BM3D方法相比,该方法具有更好的信噪比、CNR和ENL的质量指标和更好的细节保留能力,具有良好的斑点去除和结构增强效果。
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