Frequency-aware optical coherence tomography image super-resolution via
  conditional generative adversarial neural network
Xueshen Li Zhenxing Dong Hongshan Liu Jennifer J. Kang-Mieler Yuye Ling Yu Gan
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.