Optical coherence tomography angiography (OCTA) is a new imaging modality to visualize retinal microvasculature and has been readily adopted in clinics. High-resolution OCT angiograms are important to qualitatively and quantitatively identify potential biomarkers for different retinal diseases accurately. However, one significant problem of OCTA is the inevitable decrease in resolution when increasing the field-of-view given a fixed acquisition time. To address this issue, we propose a novel reference-based super-resolution (RefSR) framework to preserve the resolution of the OCT angiograms while increasing the scanning area. Specifically, textures from the normal RefSR pipeline are used to train a learnable texture generator (LTG), which is designed to generate textures according to the input. The key difference between the proposed method and traditional RefSR models is that the textures used during inference are generated by the LTG instead of being searched from a single reference image. Since the LTG is optimized throughout the whole training process, the available texture space is significantly enlarged and no longer limited to a single reference image, but extends to all textures contained in the training samples. Moreover, our proposed LTGNet does not require a reference image at the inference phase, thereby becoming invulnerable to the selection of the reference image. Both experimental and visual results show that LTGNet has superior performance and robustness over state-of-the-art methods, indicating good reliability and promise in real-life deployment. The source code will be made available upon acceptance.

翻译：光学相干断层扫描血管造影（OCTA）是一种用于可视化视网膜微血管的新型成像方式，并已在临床中得到广泛应用。高分辨率OCTA图像对于准确定性和定量识别不同视网膜疾病的潜在生物标志物至关重要。然而，OCTA的一个显著问题是在固定采集时间下，增大视野时分辨率不可避免地下降。为解决这一问题，我们提出了一种新颖的基于参考的超分辨率（RefSR）框架，在增加扫描面积的同时保持OCTA图像的分辨率。具体而言，我们利用标准RefSR流程中的纹理来训练一个可学习纹理生成器（LTG），该生成器旨在根据输入生成纹理。所提方法与传统RefSR模型的关键区别在于，推断过程中使用的纹理由LTG生成，而非从单一参考图像中搜索。由于LTG在整个训练过程中被优化，可用的纹理空间显著扩大，不再局限于单一参考图像，而是扩展到训练样本中包含的所有纹理。此外，我们提出的LTGNet在推断阶段不需要参考图像，因此对参考图像的选取不敏感。实验和视觉结果均表明，LTGNet在性能和鲁棒性上优于现有最先进方法，显示出良好的可靠性和实际部署前景。源代码将在论文被接收后公开。