  Synthetic X-ray images are simulated X-ray images projected from CT data.
High-quality synthetic X-ray images can facilitate various applications such as
surgical image guidance systems and VR training simulations. However, it is
difficult to produce high-quality arbitrary view synthetic X-ray images in
real-time due to different CT slice thickness, high computational cost, and the
complexity of algorithms. Our goal is to generate high-resolution synthetic
X-ray images in real-time by upsampling low-resolution images with deep
learning-based super-resolution methods. Reference-based Super Resolution
(RefSR) has been well studied in recent years and has shown higher performance
than traditional Single Image Super-Resolution (SISR). It can produce fine
details by utilizing the reference image but still inevitably generates some
artifacts and noise. In this paper, we introduce frequency domain loss as a
constraint to further improve the quality of the RefSR results with fine
details and without obvious artifacts. To the best of our knowledge, this is
the first paper utilizing the frequency domain for the loss functions in the
field of super-resolution. We achieved good results in evaluating our method on
both synthetic and real X-ray image datasets.
