Limited by the dynamic range of the detector, saturation artifacts usually occur in optical coherence tomography (OCT) imaging for high scattering media. The available methods are difficult to remove saturation artifacts and restore texture completely in OCT images. We proposed a deep learning-based inpainting method of saturation artifacts in this paper. The generation mechanism of saturation artifacts was analyzed, and experimental and simulated datasets were built based on the mechanism. Enhanced super-resolution generative adversarial networks were trained by the clear–saturated phantom image pairs. The perfect reconstructed results of experimental zebrafish and thyroid OCT images proved its feasibility, strong generalization, and robustness.

受探测器动态范围的限制，高散射介质的光学相干断层扫描 (OCT) 成像中通常会出现饱和伪影。现有的方法很难去除 OCT 图像中的饱和伪影并完全恢复纹理。我们在本文中提出了一种基于深度学习的饱和伪影修复方法。分析了饱和伪影的产生机制，并基于该机制建立了实验和模拟数据集。增强型超分辨率生成对抗网络通过清晰饱和的幻像图像对进行训练。实验斑马鱼和甲状腺OCT图像的完美重建结果证明了其可行性、强泛化性和鲁棒性。