A SWIN-based vision transformer for high-fidelity and high-speed imaging experiments at light sources
IntroductionHigh-speed x-ray imaging experiments at synchrotron radiation facilities enable the acquisition of spatiotemporal measurements, reaching millions of frames per second. These high data acquisition rates are often prone to noisy measurements, or in the case of slower (but less noisy) rates...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in High Performance Computing |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1537080/full |
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| author | Songyuan Tang Tekin Bicer Kamel Fezzaa Samuel Clark |
| author_facet | Songyuan Tang Tekin Bicer Kamel Fezzaa Samuel Clark |
| author_sort | Songyuan Tang |
| collection | DOAJ |
| description | IntroductionHigh-speed x-ray imaging experiments at synchrotron radiation facilities enable the acquisition of spatiotemporal measurements, reaching millions of frames per second. These high data acquisition rates are often prone to noisy measurements, or in the case of slower (but less noisy) rates, the loss of scientifically significant phenomena.MethodsWe develop a Shifted Window (SWIN)-based vision transformer to reconstruct high-resolution x-ray image sequences with high fidelity and at a high frame rate and evaluate the underlying algorithmic framework on a high-performance computing (HPC) system. We characterize model parameters that could affect the training scalability, quality of the reconstruction, and running time during the model inference stage, such as the batch size, number of input frames to the model, their composition in terms of low and high-resolution frames, and the model size and architecture.ResultsWith 3 subsequent low resolution (LR) frames and another 2 high resolution (HR) frames differing in the spatial and temporal resolutions by factors of 4 and 20, respectively, the proposed algorithm achieved an average peak signal-to-noise ratio of 37.40 dB and 35.60 dB.DiscussionFurther, the model was trained on the Argonne Leadership Computing Facility's Polaris HPC system using 40 Nvidia A100 GPUs, speeding up the end-to-end training time by about ~10 × compared to the training with beamline-local computing resources. |
| format | Article |
| id | doaj-art-7a204e411cd44259b0e268b08fc783a7 |
| institution | DOAJ |
| issn | 2813-7337 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in High Performance Computing |
| spelling | doaj-art-7a204e411cd44259b0e268b08fc783a72025-08-20T03:21:34ZengFrontiers Media S.A.Frontiers in High Performance Computing2813-73372025-05-01310.3389/fhpcp.2025.15370801537080A SWIN-based vision transformer for high-fidelity and high-speed imaging experiments at light sourcesSongyuan Tang0Tekin Bicer1Kamel Fezzaa2Samuel Clark3Advanced Photon Source, Argonne National Laboratory, Lemont, IL, United StatesData Science and Learning Division, Argonne National Laboratory, Lemont, IL, United StatesAdvanced Photon Source, Argonne National Laboratory, Lemont, IL, United StatesAdvanced Photon Source, Argonne National Laboratory, Lemont, IL, United StatesIntroductionHigh-speed x-ray imaging experiments at synchrotron radiation facilities enable the acquisition of spatiotemporal measurements, reaching millions of frames per second. These high data acquisition rates are often prone to noisy measurements, or in the case of slower (but less noisy) rates, the loss of scientifically significant phenomena.MethodsWe develop a Shifted Window (SWIN)-based vision transformer to reconstruct high-resolution x-ray image sequences with high fidelity and at a high frame rate and evaluate the underlying algorithmic framework on a high-performance computing (HPC) system. We characterize model parameters that could affect the training scalability, quality of the reconstruction, and running time during the model inference stage, such as the batch size, number of input frames to the model, their composition in terms of low and high-resolution frames, and the model size and architecture.ResultsWith 3 subsequent low resolution (LR) frames and another 2 high resolution (HR) frames differing in the spatial and temporal resolutions by factors of 4 and 20, respectively, the proposed algorithm achieved an average peak signal-to-noise ratio of 37.40 dB and 35.60 dB.DiscussionFurther, the model was trained on the Argonne Leadership Computing Facility's Polaris HPC system using 40 Nvidia A100 GPUs, speeding up the end-to-end training time by about ~10 × compared to the training with beamline-local computing resources.https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1537080/fullhigh-speed imagingspatio-temporal fusionvision transformerdistributed trainingfull-field x-ray radiography |
| spellingShingle | Songyuan Tang Tekin Bicer Kamel Fezzaa Samuel Clark A SWIN-based vision transformer for high-fidelity and high-speed imaging experiments at light sources Frontiers in High Performance Computing high-speed imaging spatio-temporal fusion vision transformer distributed training full-field x-ray radiography |
| title | A SWIN-based vision transformer for high-fidelity and high-speed imaging experiments at light sources |
| title_full | A SWIN-based vision transformer for high-fidelity and high-speed imaging experiments at light sources |
| title_fullStr | A SWIN-based vision transformer for high-fidelity and high-speed imaging experiments at light sources |
| title_full_unstemmed | A SWIN-based vision transformer for high-fidelity and high-speed imaging experiments at light sources |
| title_short | A SWIN-based vision transformer for high-fidelity and high-speed imaging experiments at light sources |
| title_sort | swin based vision transformer for high fidelity and high speed imaging experiments at light sources |
| topic | high-speed imaging spatio-temporal fusion vision transformer distributed training full-field x-ray radiography |
| url | https://www.frontiersin.org/articles/10.3389/fhpcp.2025.1537080/full |
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