Deep learning-based spatio-temporal fusion for high-fidelity ultra-high-speed X-ray radiography.

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 data sets with those obtain

SUBMITTER: Tang S 

PROVIDER: S-EPMC11892903 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature