Optical coherence tomography (OCT) suffers from speckle noise, causing the deterioration of image quality, especially in high-resolution modalities like visible light OCT (vis-OCT). The potential of conventional supervised deep learning denoising methods is limited by the difficulty of obtaining clean data. Here, we proposed an innovative self-supervised strategy called Sub2Full (S2F) for OCT despeckling without clean data. This approach works by acquiring two repeated B-scans, splitting the spectrum of the first repeat as a low-resolution input, and utilizing the full spectrum of the second repeat as the high-resolution target. The proposed method was validated on vis-OCT retinal images visualizing sublaminar structures in outer retina and demonstrated superior performance over conventional Noise2Noise and Noise2Void schemes. The code is available at https://github.com/PittOCT/Sub2Full-OCT-Denoising.

翻译：光学相干断层成像（OCT）易受散斑噪声干扰，导致图像质量下降，尤其在可见光OCT等高分辨率模态中更为突出。传统有监督深度学习方法受限于难以获取干净数据，其去噪潜力受到制约。本文提出一种无需干净数据的创新自监督策略——Sub2Full（S2F），用于OCT去斑。该方法通过获取两次重复B扫描，将首次重复的频谱分割为低分辨率输入，并将第二次重复的全频谱作为高分辨率目标。在可视化外层视网膜亚层结构的可见光OCT视网膜图像上验证了所提方法，其性能优于传统Noise2Noise和Noise2Void方案。代码已开源：https://github.com/PittOCT/Sub2Full-OCT-Denoising。