Opt Express. 2023 Mar 27;31(7):11745-11759. doi: 10.1364/OE.485097.
A frequently used technology in medical diagnosis is optical coherence tomography (OCT). However, coherent noise, also known as speckle noise, has the potential to severely reduce the quality of OCT images, which would be detrimental to the use of OCT images for disease diagnosis. In this paper, a despeckling method is proposed to effectively reduce the speckle noise in OCT images using the generalized low rank approximations of matrices (GLRAM). Specifically, the Manhattan distance (MD)-based block matching method is first used to find nonlocal similar blocks for the reference one. The left and right projection matrices shared by these image blocks are then found using the GLRAM approach, and an adaptive method based on asymptotic matrix reconstruction is proposed to determine how many eigenvectors are present in the left and right projection matrices. Finally, all the reconstructed image blocks are aggregated to create the despeckled OCT image. In addition, an edge-guided adaptive back-projection strategy is used to improve the despeckling performance of the proposed method. Experiments with synthetic and real OCT images show that the presented method performs well in both objective measurements and visual evaluation.
在医学诊断中，一种常用的技术是光学相干断层扫描（OCT）。然而，相干噪声，也称为散斑噪声，有可能严重降低 OCT 图像的质量，这将不利于 OCT 图像在疾病诊断中的应用。在本文中，提出了一种去斑方法，使用矩阵的广义低秩逼近（GLRAM）有效地减少 OCT 图像中的散斑噪声。具体来说，首先使用基于曼哈顿距离（MD）的块匹配方法找到参考块的非局部相似块。然后使用 GLRAM 方法找到这些图像块共有的左右投影矩阵，并提出了一种基于渐近矩阵重构的自适应方法来确定左右投影矩阵中存在多少个特征向量。最后，将所有重构的图像块聚合起来，生成去斑的 OCT 图像。此外，还使用边缘引导自适应反向投影策略来提高所提出方法的去斑性能。对合成和真实 OCT 图像的实验表明，所提出的方法在客观测量和视觉评估方面都表现良好。
