  In this paper, we present a medical AttentIon Denoising Super Resolution
Generative Adversarial Network (AID-SRGAN) for diographic image
super-resolution. First, we present a medical practical degradation model that
considers various degradation factors beyond downsampling. To the best of our
knowledge, this is the first composite degradation model proposed for
radiographic images. Furthermore, we propose AID-SRGAN, which can
simultaneously denoise and generate high-resolution (HR) radiographs. In this
model, we introduce an attention mechanism into the denoising module to make it
more robust to complicated degradation. Finally, the SR module reconstructs the
HR radiographs using the "clean" low-resolution (LR) radiographs. In addition,
we propose a separate-joint training approach to train the model, and extensive
experiments are conducted to show that the proposed method is superior to its
counterparts. e.g., our proposed method achieves $31.90$ of PSNR with a scale
factor of $4 \times$, which is $7.05 \%$ higher than that obtained by recent
work, SPSR [16]. Our dataset and code will be made available at:
https://github.com/yongsongH/AIDSRGAN-MICCAI2022.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning