Learn From Orientation Prior for Radiograph Super-Resolution:
  Orientation Operator Transformer
Yongsong Huang Tomo Miyazaki Xiaofeng Liu Kaiyuan Jiang Zhengmi Tang Shinichiro Omachi
Background and objective: High-resolution radiographic images play a pivotal
role in the early diagnosis and treatment of skeletal muscle-related diseases.
It is promising to enhance image quality by introducing single-image
super-resolution (SISR) model into the radiology image field. However, the
conventional image pipeline, which can learn a mixed mapping between SR and
denoising from the color space and inter-pixel patterns, poses a particular
challenge for radiographic images with limited pattern features. To address
this issue, this paper introduces a novel approach: Orientation Operator
Transformer - $O^{2}$former. Methods: We incorporate an orientation operator in
the encoder to enhance sensitivity to denoising mapping and to integrate
orientation prior. Furthermore, we propose a multi-scale feature fusion
strategy to amalgamate features captured by different receptive fields with the
directional prior, thereby providing a more effective latent representation for
the decoder. Based on these innovative components, we propose a
transformer-based SISR model, i.e., $O^{2}$former, specifically designed for
radiographic images. Results: The experimental results demonstrate that our
method achieves the best or second-best performance in the objective metrics
compared with the competitors at $\times 4$ upsampling factor. For qualitative,
more objective details are observed to be recovered. Conclusions: In this
study, we propose a novel framework called $O^{2}$former for radiological image
super-resolution tasks, which improves the reconstruction model's performance
by introducing an orientation operator and multi-scale feature fusion strategy.
Our approach is promising to further promote the radiographic image enhancement
field.