Name
Title | ||
|---|---|---|
MRI reconstruction using a joint constraint in patch-based total variational framework. |
Abstract | ||
|---|---|---|
•A joint constraint in the TV framework (JCTV) to model both local sparse and nonlocal similarity of patch is proposed.•A patch-based TV model with adaptive parameter adjustment is established in JCTV.•The LMMSE criterion is utilized to estimate the nonlocal constraint term in JCTV.•Simulation results reveal that JCTV achieves better performance than the compared methods. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.jvcir.2017.03.017 | Journal of Visual Communication and Image Representation |
Keywords | Field | DocType |
CS-MRI,Total variation,Joint constraint,LMMSE | Pattern recognition,Finite difference,Minimum mean square error,Regularization (mathematics),Artificial intelligence,Minimization algorithm,Compressed sensing,Mathematics | Journal |
Volume | Issue | ISSN |
46 | C | 1047-3203 |
Citations | PageRank | References |
2 | 0.38 | 28 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shujun Liu | 1 | 2 | 1.73 |
| Jianxin Cao | 2 | 16 | 5.21 |
| Hongqing Liu | 3 | 45 | 28.77 |
| Xiaodong Shen | 4 | 86 | 9.75 |
| Kui Zhang | 5 | 7 | 4.85 |
| WANG Pin | 6 | 3 | 1.76 |