Deep-learning-based ring artifact correction for tomographic reconstruction
Journal of Synchrotron Radiation, ISSN: 1600-5775, Vol: 30, Issue: Pt 3, Page: 620-626
2023
- 6Citations
- 6Captures
- 1Mentions
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations6
- Citation Indexes6
- CrossRef6
- Captures6
- Readers6
- Mentions1
- News Mentions1
- News1
Most Recent News
Chinese Academy of Sciences Reports Findings in Synchrotron Radiation (Deep-learning-based ring artifact correction for tomographic reconstruction)
2023 MAR 29 (NewsRx) -- By a News Reporter-Staff News Editor at Physics Daily News -- New research on Synchrotron Radiation is the subject of
Article Description
X-ray tomography has been widely used in various research fields thanks to its capability of observing 3D structures with high resolution non-destructively. However, due to the nonlinearity and inconsistency of detector pixels, ring artifacts usually appear in tomographic reconstruction, which may compromise image quality and cause nonuniform bias. This study proposes a new ring artifact correction method based on the residual neural network (ResNet) for X-ray tomography. The artifact correction network uses complementary information of each wavelet coefficient and a residual mechanism of the residual block to obtain high-precision artifacts through low operation costs. In addition, a regularization term is used to accurately extract stripe artifacts in sinograms, so that the network can better preserve image details while accurately separating artifacts. When applied to simulation and experimental data, the proposed method shows a good suppression of ring artifacts. To solve the problem of insufficient training data, ResNet is trained through the transfer learning strategy, which brings advantages of robustness, versatility and low computing cost.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85159186886&origin=inward; http://dx.doi.org/10.1107/s1600577523000917; http://www.ncbi.nlm.nih.gov/pubmed/36897392; https://journals.iucr.org/paper?S1600577523000917; https://dx.doi.org/10.1107/s1600577523000917; https://journals.iucr.org/s/issues/2023/03/00/mo5263/index.html
International Union of Crystallography (IUCr)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know