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![[Figure 2]](tv5069fig2mag.jpg)
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Figure 2
Comparison of deep learning based post-processing performance on reconstructed images from the simulated foam phantom dataset (Pelt et al., 2022  ) affected by local artifacts (noise) and non-local artifacts. The red and green insets show enlarged views of the affected areas. Peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) (Wang et al., 2004) values are provided in the top-right and lower-right corners, respectively. Deep learning based post-processing is less effective in reducing global artifacts than noise. While this figure demonstrates differences in artifact reduction based on simulation, similar challenging scenarios can occur in real-world dynamic experiments (Bührer et al., 2019) or fast scan setups (Raufaste et al., 2015; Mokso et al., 2017) at synchrotron facilities. |
![[Figure 2]](tv5069fig2.jpg)
 | JOURNAL OF SYNCHROTRON RADIATION |
ISSN: 1600-5775
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