Huang Yongqiang, Lu Zexin, Shao Zhimin, Ran Maosong, Zhou Jiliu, Fang Leyuan, Zhang Yi
Opt Express. 2019 Apr 29;27(9):12289-12307. doi: 10.1364/OE.27.012289.
Optical coherence tomography (OCT) has become a very promising diagnostic method in clinical practice, especially for ophthalmic diseases. However, speckle noise and low sampling rates have intensively reduced the quality of OCT images, which prevents the development of OCT-assisted diagnosis. Therefore, we propose a generative adversarial network-based approach (named SDSR-OCT) to simultaneously denoise and super-resolve OCT images. Moreover, we trained three different super-resolution models with different upscale factors (2× , 4× and 8×) to adapt to the corresponding downsampling rates. We also quantitatively and qualitatively compared our proposed method with some well-known algorithms. The experimental results show that our approach can effectively suppress speckle noise and can super-resolve OCT images at different scales.
光学相干断层扫描（OCT）已成为临床实践中一种非常有前景的诊断方法，尤其是对于眼科疾病。然而，散斑噪声和低采样率严重降低了OCT图像的质量，这阻碍了OCT辅助诊断的发展。因此，我们提出了一种基于生成对抗网络的方法（名为SDSR-OCT）来同时对OCT图像进行去噪和超分辨率重建。此外，我们训练了三种具有不同放大因子（2倍、4倍和8倍）的不同超分辨率模型，以适应相应的下采样率。我们还将我们提出的方法与一些知名算法进行了定量和定性比较。实验结果表明，我们的方法可以有效抑制散斑噪声，并能在不同尺度上对OCT图像进行超分辨率重建。
