Sub2Full: 分割频谱以在无干净数据的情况下增强OCT去噪效果
Sub2Full: split spectrum to boost OCT despeckling without clean data
AI总结：
针对OCT散斑噪声及干净数据获取难的问题，提出自监督去噪策略Sub2Full，利用两次重复B扫描的分割频谱与全频谱构建训练对，在vis-OCT视网膜图像上性能优于传统方法。
摘要：
光学相干断层扫描（OCT）存在散斑噪声问题，会导致图像质量下降，在可见光OCT（vis-OCT）等高分辨率模态中尤为明显。传统监督式深度学习去噪方法的潜力因难以获取干净数据而受限。本文提出了一种名为Sub2Full（S2F）的创新自监督策略，可在无干净数据的情况下实现OCT去斑。该方法通过采集两次重复的B扫描，将第一次重复扫描的频谱分割作为低分辨率输入，利用第二次重复扫描的全频谱作为高分辨率目标。研究在可可视化视网膜外亚层结构的vis-OCT视网膜图像上验证了该方法，结果表明其性能优于传统的Noise2Noise和Noise2Void方案。代码可在https://github.com/PittOCT/Sub2Full-OCT-Denoising获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
