Research on a noise-suppression super-resolution enhancement module for positron flow field images based on convolution and SwinTransformer structures
Abstract Positron emission tomography (PET) technology, with its advantages of strong γ-photon penetration and results unaffected by temperature or electromagnetic fields, has emerged as a novel non-contact monitoring technique for industrial flow fields under harsh conditions. However, dynamic samp...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-07107-1 |
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| _version_ | 1849402893204979712 |
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| author | Xiao Hui Liu Quan Xu YiBing Wang Ming Liu JianTang |
| author_facet | Xiao Hui Liu Quan Xu YiBing Wang Ming Liu JianTang |
| author_sort | Xiao Hui |
| collection | DOAJ |
| description | Abstract Positron emission tomography (PET) technology, with its advantages of strong γ-photon penetration and results unaffected by temperature or electromagnetic fields, has emerged as a novel non-contact monitoring technique for industrial flow fields under harsh conditions. However, dynamic sampling leads to a severe lack of photon data within individual time frames, resulting in an ill-posed nature of positron image reconstruction, which introduces uncertainty in noise statistical characteristics and degradation in imaging quality. This paper proposes a novel noise-suppressing super-resolution enhancement module for positron flow field imaging. The module, based on convolution and SwinTransformer structures, achieves noise reduction and enhancement of positron images under conditions of severe photon scarcity. Furthermore, a multi-loss fusion performance evaluation system is constructed to extract texture and hierarchical feature information from the images. Experimental results demonstrate that the proposed module effectively reduces image noise while preserving critical information, achieving significant improvements in the quality of generated positron flow field images. |
| format | Article |
| id | doaj-art-8a808c85b0fc4e33a2e65ce3907dedd0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-8a808c85b0fc4e33a2e65ce3907dedd02025-08-20T03:37:24ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-07107-1Research on a noise-suppression super-resolution enhancement module for positron flow field images based on convolution and SwinTransformer structuresXiao Hui0Liu Quan1Xu YiBing2Wang Ming3Liu JianTang4College of Automation Engineering, Nanjing University of Aeronautics and AstronauticsCollege of Automation Engineering, Nanjing University of Aeronautics and AstronauticsCollege of Automation Engineering, Nanjing University of Aeronautics and AstronauticsCollege of Automation Engineering, Nanjing University of Aeronautics and AstronauticsCollege of Automation Engineering, Nanjing University of Aeronautics and AstronauticsAbstract Positron emission tomography (PET) technology, with its advantages of strong γ-photon penetration and results unaffected by temperature or electromagnetic fields, has emerged as a novel non-contact monitoring technique for industrial flow fields under harsh conditions. However, dynamic sampling leads to a severe lack of photon data within individual time frames, resulting in an ill-posed nature of positron image reconstruction, which introduces uncertainty in noise statistical characteristics and degradation in imaging quality. This paper proposes a novel noise-suppressing super-resolution enhancement module for positron flow field imaging. The module, based on convolution and SwinTransformer structures, achieves noise reduction and enhancement of positron images under conditions of severe photon scarcity. Furthermore, a multi-loss fusion performance evaluation system is constructed to extract texture and hierarchical feature information from the images. Experimental results demonstrate that the proposed module effectively reduces image noise while preserving critical information, achieving significant improvements in the quality of generated positron flow field images.https://doi.org/10.1038/s41598-025-07107-1 |
| spellingShingle | Xiao Hui Liu Quan Xu YiBing Wang Ming Liu JianTang Research on a noise-suppression super-resolution enhancement module for positron flow field images based on convolution and SwinTransformer structures Scientific Reports |
| title | Research on a noise-suppression super-resolution enhancement module for positron flow field images based on convolution and SwinTransformer structures |
| title_full | Research on a noise-suppression super-resolution enhancement module for positron flow field images based on convolution and SwinTransformer structures |
| title_fullStr | Research on a noise-suppression super-resolution enhancement module for positron flow field images based on convolution and SwinTransformer structures |
| title_full_unstemmed | Research on a noise-suppression super-resolution enhancement module for positron flow field images based on convolution and SwinTransformer structures |
| title_short | Research on a noise-suppression super-resolution enhancement module for positron flow field images based on convolution and SwinTransformer structures |
| title_sort | research on a noise suppression super resolution enhancement module for positron flow field images based on convolution and swintransformer structures |
| url | https://doi.org/10.1038/s41598-025-07107-1 |
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