Optical coherence tomography (OCT) has stimulated a wide range of medical
image-based diagnosis and treatment in fields such as cardiology and
ophthalmology. Such applications can be further facilitated by deep
learning-based super-resolution technology, which improves the capability of
resolving morphological structures. However, existing deep learning-based
method only focuses on spatial distribution and disregard frequency fidelity in
image reconstruction, leading to a frequency bias. To overcome this limitation,
we propose a frequency-aware super-resolution framework that integrates three
critical frequency-based modules (i.e., frequency transformation, frequency
skip connection, and frequency alignment) and frequency-based loss function
into a conditional generative adversarial network (cGAN). We conducted a
large-scale quantitative study from an existing coronary OCT dataset to
demonstrate the superiority of our proposed framework over existing deep
learning frameworks. In addition, we confirmed the generalizability of our
framework by applying it to fish corneal images and rat retinal images,
demonstrating its capability to super-resolve morphological details in eye
imaging.

光学相干断层扫描（OCT）通过提高形态结构解析能力，利用深度学习技术实现了基于医学图像的诊断与治疗。为了克服现有深度学习方法在图像重建中的频率偏差问题，本文提出了一种结合频率感知超分辨率框架，通过集成三个关键的基于频率的模块（频率变换、频率跳跃连接和频率对齐）及频率损失函数到条件生成对抗网络（cGAN）的方法。通过对一个现有的冠状动脉 OCT 数据集进行大规模定量研究，证明了我们提出的框架优于现有的深度学习框架。此外，我们还将该框架应用于鱼角膜图像和大鼠视网膜图像，证明了它在眼科成像中对解析形态细节的能力。