{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-learning-based-spatio-temporal-fusion","title":"Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed x-ray radiography","arxiv_id":"2411.18441","date":"2024-11-27","proceeding":null,"authors":["Songyuan Tang","Tekin Bicer","Tao Sun","Kamel Fezzaa","Samuel J. Clark"],"abstract":"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 datasets 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 3 subsequent images from the low resolution (LR) sequence of a 4-time lower spatial resolution and another 2 images from the high resolution (HR) sequence of a 20-time lower frame rate, the proposed approach achieved an average PSNR 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 scientific value of the UHS x-ray imaging experiments.","url_abs":"https://arxiv.org/abs/2411.18441v1","url_pdf":"https://arxiv.org/pdf/2411.18441v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-learning-based-spatio-temporal-fusion","repo_url":"https://github.com/xray-imaging/xfusion","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"ssim","task_name":"SSIM"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2411.18441","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}