Li Wanyue, Liu Guangxing, He Yi, Wang Jing, Kong Wen, Shi Guohua
University of Science and Technology of China, Hefei, 230041, China.
Jiangsu Key Laboratory of Medical Optics, Suzhou, 215163, China.
Biomed Opt Express. 2020 Jan 14;11(2):831-849. doi: 10.1364/BOE.380224. eCollection 2020 Feb 1.
The adaptive optics (AO) technique is widely used to compensate for ocular aberrations and improve imaging resolution. However, when affected by intraocular scatter, speckle noise, and other factors, the quality of the retinal image will be degraded. To effectively improve the image quality without increasing the imaging system's complexity, the post-processing method of image deblurring is adopted. In this study, we proposed a conditional adversarial network-based method for directly learning an end-to-end mapping between blurry and restored AO retinal images. The proposed model was validated on synthetically generated AO retinal images and real retinal images. The restoration results of synthetic images were evaluated with the metrics of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), perceptual distance, and error rate of cone counting. Moreover, the blind image quality index (BIQI) was used as the no-reference image quality assessment (NR-IQA) algorithm to evaluate the restoration results on real AO retinal images. The experimental results indicate that the images restored by the proposed method have sharper quality and higher signal-to-noise ratio (SNR) when compared with other state-of-the-art methods, which has great practical significance for clinical research and analysis.
自适应光学（AO）技术被广泛用于补偿眼部像差并提高成像分辨率。然而，当受到眼内散射、散斑噪声和其他因素影响时，视网膜图像的质量会下降。为了在不增加成像系统复杂性的情况下有效提高图像质量，采用了图像去模糊的后处理方法。在本研究中，我们提出了一种基于条件对抗网络的方法，用于直接学习模糊和恢复后的AO视网膜图像之间的端到端映射。所提出的模型在合成生成的AO视网膜图像和真实视网膜图像上进行了验证。合成图像的恢复结果通过峰值信噪比（PSNR）、结构相似性（SSIM）、感知距离和视锥细胞计数错误率等指标进行评估。此外，使用盲图像质量指数（BIQI）作为无参考图像质量评估（NR-IQA）算法来评估真实AO视网膜图像的恢复结果。实验结果表明，与其他现有方法相比，所提方法恢复的图像质量更清晰，信噪比（SNR）更高，这对临床研究和分析具有重要的实际意义。
