Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiography
Full-field ultra-high-speed (UHS) X-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of X-ray videos with distinct configurations has not been fully exploited. In this paper, we...
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| Format: | Article |
| Language: | English |
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International Union of Crystallography
2025-03-01
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| Series: | Journal of Synchrotron Radiation |
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| Online Access: | https://journals.iucr.org/paper?S1600577525000323 |
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| author | Songyuan Tang Tekin Bicer Tao Sun Kamel Fezzaa Samuel J. Clark |
| author_facet | Songyuan Tang Tekin Bicer Tao Sun Kamel Fezzaa Samuel J. Clark |
| author_sort | Songyuan Tang |
| collection | DOAJ |
| description | Full-field ultra-high-speed (UHS) X-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of X-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-temporal fusion (STF) framework to fuse two complementary sequences of X-ray images and reconstruct the target image sequence with high spatial resolution, high frame rate and high fidelity. We applied a transfer learning strategy to train the model and compared the peak signal-to-noise ratio (PSNR), average absolute difference (AAD) and structural similarity (SSIM) of the proposed framework on two independent X-ray data sets with those obtained from a baseline deep learning model, a Bayesian fusion framework and the bicubic interpolation method. The proposed framework outperformed the other methods with various configurations of the input frame separations and image noise levels. With three subsequent images from the low-resolution (LR) sequence of a four times lower spatial resolution and another two images from the high-resolution (HR) sequence of a 20 times lower frame rate, the proposed approach achieved average PSNRs of 37.57 dB and 35.15 dB, respectively. When coupled with the appropriate combination of high-speed cameras, the proposed approach will enhance the performance and therefore the scientific value of UHS X-ray imaging experiments. |
| format | Article |
| id | doaj-art-c9c76b08a8b34108b4aef6b52037026a |
| institution | OA Journals |
| issn | 1600-5775 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | International Union of Crystallography |
| record_format | Article |
| series | Journal of Synchrotron Radiation |
| spelling | doaj-art-c9c76b08a8b34108b4aef6b52037026a2025-08-20T01:57:35ZengInternational Union of CrystallographyJournal of Synchrotron Radiation1600-57752025-03-0132243244110.1107/S1600577525000323tv5068Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiographySongyuan Tang0Tekin Bicer1Tao Sun2Kamel Fezzaa3Samuel J. Clark4Advanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USAAdvanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USADepartment of Mechanical Engineering, Northwestern University, Evanston, IL 60208, USAAdvanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USAAdvanced Photon Source, Argonne National Laboratory, Lemont, IL 60439, USAFull-field ultra-high-speed (UHS) X-ray imaging experiments have been well established to characterize various processes and phenomena. However, the potential of UHS experiments through the joint acquisition of X-ray videos with distinct configurations has not been fully exploited. In this paper, we investigate the use of a deep learning-based spatio-temporal fusion (STF) framework to fuse two complementary sequences of X-ray images and reconstruct the target image sequence with high spatial resolution, high frame rate and high fidelity. We applied a transfer learning strategy to train the model and compared the peak signal-to-noise ratio (PSNR), average absolute difference (AAD) and structural similarity (SSIM) of the proposed framework on two independent X-ray data sets with those obtained from a baseline deep learning model, a Bayesian fusion framework and the bicubic interpolation method. The proposed framework outperformed the other methods with various configurations of the input frame separations and image noise levels. With three subsequent images from the low-resolution (LR) sequence of a four times lower spatial resolution and another two images from the high-resolution (HR) sequence of a 20 times lower frame rate, the proposed approach achieved average PSNRs of 37.57 dB and 35.15 dB, respectively. When coupled with the appropriate combination of high-speed cameras, the proposed approach will enhance the performance and therefore the scientific value of UHS X-ray imaging experiments.https://journals.iucr.org/paper?S1600577525000323x-ray imagingdeep learninghigh-speed imagingspatio-temporal fusionfull-field x-ray radiography |
| spellingShingle | Songyuan Tang Tekin Bicer Tao Sun Kamel Fezzaa Samuel J. Clark Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiography Journal of Synchrotron Radiation x-ray imaging deep learning high-speed imaging spatio-temporal fusion full-field x-ray radiography |
| title | Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiography |
| title_full | Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiography |
| title_fullStr | Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiography |
| title_full_unstemmed | Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiography |
| title_short | Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiography |
| title_sort | deep learning based spatio temporal fusion for high fidelity ultra high speed x ray radiography |
| topic | x-ray imaging deep learning high-speed imaging spatio-temporal fusion full-field x-ray radiography |
| url | https://journals.iucr.org/paper?S1600577525000323 |
| work_keys_str_mv | AT songyuantang deeplearningbasedspatiotemporalfusionforhighfidelityultrahighspeedxrayradiography AT tekinbicer deeplearningbasedspatiotemporalfusionforhighfidelityultrahighspeedxrayradiography AT taosun deeplearningbasedspatiotemporalfusionforhighfidelityultrahighspeedxrayradiography AT kamelfezzaa deeplearningbasedspatiotemporalfusionforhighfidelityultrahighspeedxrayradiography AT samueljclark deeplearningbasedspatiotemporalfusionforhighfidelityultrahighspeedxrayradiography |