AI 中文总结
本研究针对OCT系统中光谱不连续导致的难题，提出用神经网络融合带间隙的光谱生成的OCT图像，可重建超分辨率且降噪效果随带隙增大的高质量图像，还可作为掩码自编码器用于宽带场景的自监督图像质量增强。
AI 中文摘要
傅里叶域（FD）光学相干断层扫描（OCT）依赖宽带光源来最大化轴向分辨率和图像质量，但这类激光会显著提高设备成本，或无法在所需波长和带宽范围内获取。一个潜在解决方案是将多个更实惠、单带宽更低的光源集成到单一系统中，不过若生成的光谱存在不连续性，就会出现难题。本研究提出一种方法，可使用神经网络将任意数量、可能存在非重叠间隙的光谱生成的OCT图像进行融合。与低分辨率输入图像相比，重建的B-scan具备超分辨率，能保留精细、低对比度的细节及边缘，且降噪效果随带隙增大而增强。该方法有望为使用光谱不相交、低带宽光源实现高质量OCT成像迈出重要一步；在宽带场景中，它可直接作为傅里叶域掩码自编码器用于自监督图像质量增强。
英文摘要
Fourier-domain (FD) optical coherence tomography (OCT) depends on broadband sources to maximize axial resolution and image quality. However, these lasers significantly drive device cost or may be unavailable at desired wavelength and bandwidth ranges. A potential solution lies in integrating multiple, more affordable sources with lower individual bandwidth into a single system. However, difficulties arise if the resulting spectrum exhibits discontinuities. In this letter, we present a method that can combine OCT images from a flexible number of spectra with arbitrary, possibly non-overlapping gaps using a neural network. Compared to low-resolution input images, reconstructed B-scans are super-resolved, preserve even fine and low-contrast details and edges, and exhibit strong noise reduction that increases with the band gap. The proposed method could thereby provide a major step towards high-quality OCT imaging using spectrally disjoint, low-bandwidth sources. In broadband settings, it can be directly applied as a Fourier-domain masked autoencoder for self-supervised image quality enhancement.
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