Optical coherence tomography (OCT) is a rapidly developing non-invasive three dimensional imaging approach, and it has been widely used in examination and diagnosis of eye diseases. However, speckle noise are often inherited from image acquisition process, and may obscure the anatomical structure, such as the retinal layers. In this paper, we propose a novel method to reduce the speckle noise in 3D OCT scans, by introducing a new super-resolution approach. It uses a multi-frame fusion mechanism that merges multiple scans for the same scene, and utilizes the movements of sub-pixels to recover missing signals in one pixel, which significantly improves the image quality. To evaluate the effectiveness of the proposed speckle noise reduction method, we have applied it for the application of retinal layer segmentation. Results show that the proposed method has produced promising enhancement performance, and enable deep learning-based methods to obtain more accurate retinal layer segmentation results.

光学相干断层扫描（OCT）是一种快速发展的无创三维成像方法，已广泛应用于眼部疾病的检查和诊断。然而，散斑噪声通常是从图像采集过程中继承下来的，并且可能会掩盖解剖结构，例如视网膜层。在本文中，我们提出了一种通过引入新的超分辨率方法来减少 3D OCT 扫描中散斑噪声的新方法。它采用多帧融合机制，将同一场景的多次扫描融合在一起，利用子像素的运动来恢复一个像素内丢失的信号，显着提高了图像质量。为了评估所提出的散斑降噪方法的有效性，我们已将其应用于视网膜层分割的应用。