光学相干层析成像(optical coherence tomography,OCT)运用低相干光源,得到的图像会不可避免地受到散斑噪声的影响.为了获得高信噪比和高分辨率的OCT图像,提出了一种基于生成对抗网络的超分辨率重建网络模型,以同时实现对OCT图像的去噪和超分辨率重建.将所提模型在OCT图像数据集上进行实验,并与一些著名的模型进行了定量和定性比较.研究结果表明,该模型峰值信噪比(peak signal-to-noise ratio,PSNR)平均值表现居中,学习感知图像块相似度平均值表现优越,表明该模型能更好地恢复图像细节,更有利于进行医学图像诊断,提高临床诊断准确率.
Optical coherence tomography (OCT) uses a low-coherence optical source, and the images obtained are inevitably affected by scattering noise. To obtain OCT images with high signal-to-noise ratio and high resolution, a super-resolution reconstruction network model is proposed based on the generative adversarial network to simultaneously achieve denoising and super-resolution reconstruction of OCT images. This model is evaluated on an OCT image dataset and compared with some well-established models quantitatively and qualitatively. The results show that the average peak signal-to-noise ratio (PSNR) of this model is in the intermediate range and that the average similarity value of the learned perceptual image block is superior. This indicates that this model effectively recovers the image details and is conducive to the diagnosis of medical images, thus improving the accuracy of clinical diagnosis.
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