Deep learning CT image restoration using system blur and noise models
Results demonstrate the efficacy of providing a deep learning model with auxiliary inputs, representing system blur and noise characteristics, to enhance the performance of the model in image restoration tasks.
Authors
- Grace J. Gang (ORCID: https://orcid.org/0000-0002-3882-0210)
- J. Webster Stayman
- Yijie Yuan
Institutions
- Johns Hopkins University (US)
- University of Pennsylvania (US)
Publication Details
- Journal
- Journal of Medical Imaging
- Published
- 2025-02-03
- DOI
- https://doi.org/10.1117/1.jmi.12.1.014003
- Citations
- 1
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.67
Funders
- National Institute of Biomedical Imaging and Bioengineering