Huang Y, Miyazaki T, Liu X, Jiang K, Tang Z, Omachi S. Learn from orientation prior for radiograph super-resolution: Orientation operator transformer.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024;
245:108000. [PMID:
38237449 DOI:
10.1016/j.cmpb.2023.108000]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 11/09/2023] [Accepted: 12/26/2023] [Indexed: 02/15/2024]
Abstract
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 - O2former.
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., O2former, 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 ×4 upsampling factor. For qualitative, more objective details are observed to be recovered.
CONCLUSIONS
In this study, we propose a novel framework called O2former 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.
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