  For optical coherence tomography angiography (OCTA) images, a limited
scanning rate leads to a trade-off between field-of-view (FOV) and imaging
resolution. Although larger FOV images may reveal more parafoveal vascular
lesions, their application is greatly hampered due to lower resolution. To
increase the resolution, previous works only achieved satisfactory performance
by using paired data for training, but real-world applications are limited by
the challenge of collecting large-scale paired images. Thus, an unpaired
approach is highly demanded. Generative Adversarial Network (GAN) has been
commonly used in the unpaired setting, but it may struggle to accurately
preserve fine-grained capillary details, which are critical biomarkers for
OCTA. In this paper, our approach aspires to preserve these details by
leveraging the frequency information, which represents details as
high-frequencies ($\textbf{hf}$) and coarse-grained backgrounds as
low-frequencies ($\textbf{lf}$). In general, we propose a GAN-based unpaired
super-resolution method for OCTA images and exceptionally emphasize
$\textbf{hf}$ fine capillaries through a dual-path generator. To facilitate a
precise spectrum of the reconstructed image, we also propose a frequency-aware
adversarial loss for the discriminator and introduce a frequency-aware focal
consistency loss for end-to-end optimization. Experiments show that our method
outperforms other state-of-the-art unpaired methods both quantitatively and
visually.

Image and Video Processing;Computer Vision and Pattern Recognition