Fei Xiao, Zhao Junlei, Zhao Haoxin, Yun Dai, Zhang Yudong
The Laboratory on Adaptive Optics, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.
Key Laboratory on Adaptive Optics, Chinese Academy of Sciences, Chengdu 610209, China.
Biomed Opt Express. 2017 Nov 16;8(12):5675-5687. doi: 10.1364/BOE.8.005675. eCollection 2017 Dec 1.
The adaptive optics (AO) can be used to compensate for ocular aberrations to achieve near diffraction limited high-resolution retinal images. However, many factors such as the limited aberration measurement and correction accuracy with AO, intraocular scatter, imaging noise and so on will degrade the quality of retinal images. Image post processing is an indispensable and economical method to make up for the limitation of AO retinal imaging procedure. In this paper, we proposed a deep learning method to restore the degraded retinal images for the first time. The method directly learned an end-to-end mapping between the blurred and restored retinal images. The mapping was represented as a deep convolutional neural network that was trained to output high-quality images directly from blurry inputs without any preprocessing. This network was validated on synthetically generated retinal images as well as real AO retinal images. The assessment of the restored retinal images demonstrated that the image quality had been significantly improved.
自适应光学（AO）可用于补偿眼部像差，以获得接近衍射极限的高分辨率视网膜图像。然而，诸如AO的像差测量和校正精度有限、眼内散射、成像噪声等诸多因素会降低视网膜图像的质量。图像后处理是弥补AO视网膜成像过程局限性的一种不可或缺且经济的方法。在本文中，我们首次提出了一种深度学习方法来恢复退化的视网膜图像。该方法直接学习模糊视网膜图像和恢复后的视网膜图像之间的端到端映射。该映射由一个深度卷积神经网络表示，该网络经过训练可直接从模糊输入中输出高质量图像，无需任何预处理。该网络在合成生成的视网膜图像以及真实的AO视网膜图像上得到了验证。对恢复后的视网膜图像的评估表明，图像质量得到了显著提高。
