Learn From Orientation Prior for Radiograph Super-Resolution: Orientation Operator Transformer

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...

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Bibliographic Details
Published in:arXiv.org 2023-12
Main Authors: Huang, Yongsong, Miyazaki, Tomo, Liu, Xiaofeng, Jiang, Kaiyuan, Tang, Zhengmi, Omachi, Shinichiro
Format: Article
Language:English
Subjects:
Image enhancement
Image quality
Image reconstruction
Image resolution
Mapping
Medical imaging
Noise reduction
Orientation
Sensitivity enhancement
Transformers
Online Access:Get full text
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Summary: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.
ISSN:2331-8422