Optical coherence tomography (OCT) is a non-invasive and effective tool for the imaging of retinal tissue. However, the heavy speckle noise, resulting from multiple scattering of the light waves, obscures important morphological structures and impairs the clinical diagnosis of ocular diseases. In this paper, we propose a novel and powerful model known as tensor ring decomposition-guided dictionary learning (TRGDL) for OCT image denoising, which can simultaneously utilize two useful complementary priors, i.e., three-dimensional low-rank and sparsity priors, under a unified framework. Specifically, to effectively use the strong correlation between nearby OCT frames, we construct the OCT group tensors by extracting cubic patches from OCT images and clustering similar patches. Then, since each created OCT group tensor has a low-rank structure, to exploit spatial, non-local, and its temporal correlations in a balanced way, we enforce the TR decomposition model on each OCT group tensor. Next, to use the beneficial three-dimensional inter-group sparsity, we learn shared dictionaries in both spatial and temporal dimensions from all of the stacked OCT group tensors. Furthermore, we develop an effective algorithm to solve the resulting optimization problem by using two efficient optimization approaches, including proximal alternating minimization and the alternative direction method of multipliers. Finally, extensive experiments on OCT datasets from various imaging devices are conducted to prove the generality and usefulness of the proposed TRGDL model. Experimental simulation results show that the suggested TRGDL model outperforms state-of-the-art approaches for OCT image denoising both qualitatively and quantitatively.

中文翻译：

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用于 OCT 图像去噪的张量环分解引导字典学习


光学相干断层扫描（OCT）是一种非侵入性且有效的视网膜组织成像工具。然而，光波多次散射产生的严重散斑噪声掩盖了重要的形态结构，损害了眼部疾病的临床诊断。在本文中，我们提出了一种新颖且强大的模型，称为张量环分解引导字典学习（TRGDL），用于 OCT 图像去噪，该模型可以同时利用两个有用的互补先验，即三维低秩和稀疏先验，在一个统一的框架。具体来说，为了有效利用附近 OCT 帧之间的强相关性，我们通过从 OCT 图像中提取立方块并对相似块进行聚类来构造 OCT 组张量。然后，由于每个创建的 OCT 组张量都具有低秩结构，为了以平衡的方式利用空间、非局部及其时间相关性，我们在每个 OCT 组张量上强制执行 TR 分解模型。接下来，为了利用有益的三维组间稀疏性，我们从所有堆叠的 OCT 组张量中学习空间和时间维度上的共享字典。此外，我们开发了一种有效的算法来通过使用两种有效的优化方法来解决由此产生的优化问题，包括近端交替最小化和乘法器的替代方向方法。最后，对来自各种成像设备的 OCT 数据集进行了广泛的实验，以证明所提出的 TRGDL 模型的通用性和实用性。实验模拟结果表明，所提出的 TRGDL 模型在定性和定量方面均优于 OCT 图像去噪的最先进方法。