Optical coherence tomography (OCT) is a high-resolution optical imaging technology that has been widely used in various fields, such as medical diagnostics, biological systems and materials science. However, because of the low-coherence interference of light, OCT images are inevitably destroyed by speckle noise. To remove speckle noise, an iterative contraction algorithm based on Chi-square similarity and fuzzy logic is proposed in this paper. An OCT image is first divided into a lot of overlapping image blocks, and a Chi-square distance similar block matching is utilized to form a low rank group matrix. Then, the singular value decomposition of the group matrix is performed, and the singular values are contracted by different weights with fuzzy logic. Finally, a pixel intensity fuzzy classification backward projection technique and an adaptive iterative stopping strategy are used to enhance the denoising effect. Extensive experiments are performed on 18 OCT images of the human retina. Compared with several state-of-the-art denoising algorithms, the experimental results show that the proposed algorithm obtains better objective indicators and visual inspection.

光学相干断层扫描（OCT）是一种高分辨率光学成像技术，已广泛应用于医学诊断、生物系统和材料科学等各个领域。然而，由于光的低相干干涉，OCT图像不可避免地被散斑噪声破坏。针对散斑噪声，本文提出了一种基于卡方相似度和模糊逻辑的迭代收缩算法。一张OCT图像首先被划分为许多重叠的图像块，利用卡方距离相似块匹配形成低秩组矩阵。然后对群矩阵进行奇异值分解，用模糊逻辑对奇异值进行不同的权重压缩。最后，使用像素强度模糊分类反向投影技术和自适应迭代停止策略来增强去噪效果。对人类视网膜的 18 张 OCT 图像进行了大量实验。与几种最先进的去噪算法相比，实验结果表明，该算法获得了更好的客观指标和视觉检查。