Name
Title | ||
|---|---|---|
A Gaussian Mixture MRF for Model-Based Iterative Reconstruction with Applications to Low-Dose X-ray CT. |
Abstract | ||
|---|---|---|
Markov random fields (MRFs) have been widely used as prior models in various inverse problems such as tomographic reconstruction. While MRFs provide a simple and often effective way to model the spatial dependencies in images, they suffer from the fact that parameter estimation is difficult. In practice, this means that MRFs typically have very simple structure that cannot completely capture the s... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TCI.2016.2582042 | IEEE Transactions on Computational Imaging |
Keywords | DocType | Volume |
Computational modeling,Image reconstruction,Adaptation models,Optimization,Computed tomography | Journal | 2 |
Issue | ISSN | Citations |
3 | 2573-0436 | 4 |
PageRank | References | Authors |
0.39 | 30 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruoqiao Zhang | 1 | 24 | 2.71 |
| Dong Hye Ye | 2 | 450 | 24.29 |
| Debashish Pal | 3 | 12 | 1.64 |
| Jean-Baptiste Thibault | 4 | 40 | 6.78 |
| Ken D. Sauer | 5 | 576 | 90.54 |
| Charles A. Bouman | 6 | 2740 | 473.62 |