Optical coherence tomography (OCT) is widely used for diagnosing and monitoring retinal disorders. However, despite hardware improvements, its scans are still highly affected by speckle noise. Speckle noise reduces quality of measurements and decreases reliability of further instrumentation. Recent OCT denoising methods are often complex and computationally inefficient, despite their valid performance. These methods can be used as reference methods to train deep auto-encoders (AEs). AE networks can learn important structural features of OCT images that have been denoised with these reference methods and use features to reconstruct or denoise corrupted ones. In this way, a well-trained AE can efficiently mimic that reference denoising method. In this study, we implemented a lightweight convolutional AE to mimic a recent state-of-the-art method in OCT image denoising. We evaluated the performance of AE for various test data sets using both visual inspection and quantitative metrics. Presented results confirmed good performance of the proposed AE in despeckling OCT scans. Results revealed the generality, computationally efficiency, and device independence property of the proposed method. These features make the proposed network applicable in real time, mobile application due to its high denoising speed and low memory usage.

中文翻译：

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用于视网膜光学相干断层扫描图像降噪的轻型模拟卷积自动编码器

光学相干断层扫描（OCT）被广泛用于诊断和监测视网膜疾病。但是，尽管对硬件进行了改进，但其扫描仍然受到斑点噪声的严重影响。斑点噪声会降低测量质量，并降低后续仪器的可靠性。尽管OCT去噪方法具有有效的性能，但它们通常很复杂且计算效率低下。这些方法可用作训练深度自动编码器（AE）的参考方法。AE网络可以学习使用这些参考方法去噪的OCT图像的重要结构特征，并使用特征来重建或去噪损坏的图像。这样，训练有素的AE可以有效地模仿该参考去噪方法。在这项研究中，我们实现了轻量级的卷积AE，以模仿OCT图像降噪中的最新技术。我们使用目测和定量指标评估了各种测试数据集的AE性能。提出的结果证实了建议的AE在去除斑点的OCT扫描中的良好性能。结果揭示了该方法的通用性，计算效率和设备独立性。这些特性使该网络具有高去噪速度和低内存使用率，因此可以实时，实时地应用于移动应用中。计算效率，以及该方法的设备独立性。这些特性使该网络具有高去噪速度和低内存使用率，因此可以实时，实时地应用于移动应用中。计算效率，以及该方法的设备独立性。这些特性使该网络具有高去噪速度和低内存使用率，因此可以实时，实时地应用于移动应用中。