Ge Chenkun, Yu Xiaojun, Yuan Miao, Fan Zeming, Chen Jinna, Shum Perry Ping, Liu Linbo
School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, 710072, China.
Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen, Guangzhou, 51800, China.
Biomed Opt Express. 2024 Jan 30;15(2):1233-1252. doi: 10.1364/BOE.515520. eCollection 2024 Feb 1.
Optical coherence tomography (OCT) inevitably suffers from the influence of speckles originating from multiple scattered photons owing to its low-coherence interferometry property. Although various deep learning schemes have been proposed for OCT despeckling, they typically suffer from the requirement for ground-truth images, which are difficult to collect in clinical practice. To alleviate the influences of speckles without requiring ground-truth images, this paper presents a self-supervised deep learning scheme, namely, Self2Self strategy (S2Snet), for OCT despeckling using a single noisy image. Specifically, in this study, the main deep learning architecture is the Self2Self network, with its partial convolution being updated with a gated convolution layer. Specifically, both the input images and their Bernoulli sampling instances are adopted as network input first, and then, a devised loss function is integrated into the network to remove the background noise. Finally, the denoised output is estimated using the average of multiple predicted outputs. Experiments with various OCT datasets are conducted to verify the effectiveness of the proposed S2Snet scheme. Results compared with those of the existing methods demonstrate that S2Snet not only outperforms those existing self-supervised deep learning methods but also achieves better performances than those non-deep learning ones in different cases. Specifically, S2Snet achieves an improvement of 3.41% and 2.37% for PSNR and SSIM, respectively, as compared to the original Self2Self network, while such improvements become 19.9% and 22.7% as compared with the well-known non-deep learning NWSR method.
光学相干断层扫描（OCT）由于其低相干干涉特性，不可避免地受到来自多次散射光子的散斑影响。尽管已经提出了各种深度学习方案用于OCT去噪，但它们通常需要真实图像，而在临床实践中很难收集到真实图像。为了在不需要真实图像的情况下减轻散斑的影响，本文提出了一种自监督深度学习方案，即Self2Self策略（S2Snet），用于使用单个噪声图像进行OCT去噪。具体而言，在本研究中，主要的深度学习架构是Self2Self网络，其部分卷积通过门控卷积层进行更新。具体来说，首先将输入图像及其伯努利采样实例都作为网络输入，然后，将设计的损失函数集成到网络中以去除背景噪声。最后，使用多个预测输出的平均值来估计去噪后的输出。通过对各种OCT数据集进行实验，验证了所提出的S2Snet方案的有效性。与现有方法的结果比较表明，S2Snet不仅优于现有的自监督深度学习方法，而且在不同情况下比非深度学习方法具有更好的性能。具体而言，与原始的Self2Self网络相比，S2Snet的PSNR和SSIM分别提高了3.41%和2.37%，而与著名的非深度学习NWSR方法相比，这些提高分别达到了19.9%和22.7%。
