基于优化生成对抗网络的OCT图像去噪和超分辨率重建

doi:10.12066/j.issn.1007-2861.2566

Abstract

Abstract: 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.

Key words: optical coherence tomography (OCT), super-resolution, image denoising, generative adversarial network

CLC Number:

TP391

ZHAO Jing, WANG Chi, YU Zhukai, XU Jingjing. OCT image denoising and super-resolution reconstruction based on optimized generative adversarial networks[J]. Journal of Shanghai University（Natural Science Edition）, 2026, 32(2): 251-260.

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OCT image denoising and super-resolution reconstruction based on optimized generative adversarial networks

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