A Frequency Domain Constraint for Synthetic and Real X-ray Image Super Resolution

TL;DR

This paper introduces a novel frequency domain loss function to enhance deep learning-based super-resolution of synthetic and real X-ray images, achieving higher quality results with fine details and fewer artifacts.

Contribution

It is the first to utilize frequency domain loss functions in super-resolution, improving detail preservation and artifact reduction in X-ray image super-resolution.

Findings

Improved image quality with finer details

Reduced artifacts and noise in super-resolved images

Effective on both synthetic and real datasets

Abstract

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.