基于稳健性主成分分析算法的光学相干层析成像去除散斑噪声的研究

Speckle Noise Reduction of Optical Coherence Tomography Based on Robust Principle Component Analysis Algorithm

为了消除光学相干层析成像(OCT)系统中存在的大量散斑噪声,引入了稳健性主成分分析(RPCA)算法。通过分析生物组织在OCT中散斑的产生机制,从而了解OCT系统中散斑噪声的特点。结合OCT系统自身的特点,证明基于RPCA算法的低秩矩阵恢复模型对OCT系统消除散斑噪声有良好的适用性。利用RPCA算法,可以得到将OCT原始图像分解成散斑噪声图像和样品截面图像的最佳估计。RPCA算法能在分离散斑噪声的同时,保留样品自身结构的散斑图样,有效地避免了伪影的生成。通过对比处理后和处理前的图像,结果表明,RPCA算法能够有效地抑制散斑噪声,提高信噪比,改善OCT图像效果。

Robust principle component analysis（RPCA）algorithm is introduced to eliminate the mass speckle noise in optical coherence tomography（OCT）system.We understand the characteristics of speckle noise in OCT system by analyzing the speckle generation mechanism in OCT system.Combining the characteristics of OCT system itself,the low-rank matrix recovered model based on RPCA algorithm is proved to be suitable for the speckle noise reduction in OCT system.The best estimation which decomposes the original image of OCT into speckle noise image and sample cross section image can be obtained based on the RPCA algorithm.RPCA algorithm can retain the speckle patterns of the sample＇s own structure while separating the speckle noise,and avoid the generation of the artifact effectively.The result shows that RPCA algorithm can effectively suppress the speckle noise,enhance the signal-tonoise ratio,and improve the effect of OCT images,through comparing the images before and after processing.