Deep learning-based inpainting of saturation artifacts in optical coherence tomography images

oleh: Muyun Hu, Zhuoqun Yuan, Di Yang, Jingzhu Zhao, Yanmei Liang

Format: Article
Diterbitkan: World Scientific Publishing 2024-05-01

Deskripsi

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.