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Optical coherence tomography (OCT) has stimulated a wide range of medicalimage-based diagnosis and treatment in fields such as cardiology andophthalmology. Such applications can be further facilitated by deeplearning-based super-resolution technology, which improves the capability ofresolving morphological structures. However, existing deep learning-basedmethod only focuses on spatial distribution and disregard frequency fidelity inimage reconstruction, leading to a frequency bias. To overcome this limitation,we propose a frequency-aware super-resolution framework that integrates threecritical frequency-based modules (i.e., frequency transformation, frequencyskip connection, and frequency alignment) and frequency-based loss functioninto a conditional generative adversarial network (cGAN). We conducted alarge-scale quantitative study from an existing coronary OCT dataset todemonstrate the superiority of our proposed framework over existing deeplearning frameworks. In addition, we confirmed the generalizability of ourframework by applying it to fish corneal images and rat retinal images,demonstrating its capability to super-resolve morphological details in eyeimaging.

Frequency-aware optical coherence tomography image super-resolution via conditional generative adversarial neural network