Name
Title | ||
|---|---|---|
Denoising 3-D magnitude magnetic resonance images based on weighted nuclear norm minimization. |
Abstract | ||
|---|---|---|
•A novel denoising algorithm is developed for 3-D magnetic resonance images.•This algorithm is based on low-rank matrix approximation (LRMA).•The closed-form solution of LRMA is got from weighted nuclear norm minimization.•The solution shrinks different singular value with a different threshold.•A non local means filter is used as a postprocessing step for better visual effect. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.bspc.2017.01.016 | Biomedical Signal Processing and Control |
Keywords | Field | DocType |
Non-local similarity,Low-rank matrix approximation,Weighted nuclear norm minimization,MRI denoising | Noise reduction,Magnitude (mathematics),Singular value decomposition,Singular value,Pattern recognition,Matrix (mathematics),Non-local means,Regularization (mathematics),Artificial intelligence,Lexicographical order,Mathematics | Journal |
Volume | ISSN | Citations |
34 | 1746-8094 | 2 |
PageRank | References | Authors |
0.35 | 30 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yi Xia | 1 | 31 | 5.65 |
| Qingwei Gao | 2 | 26 | 5.68 |
| Nan Cheng | 3 | 5 | 0.77 |
| Yixiang Lu | 4 | 44 | 5.93 |
| Dexiang Zhang | 5 | 46 | 6.94 |
| Qiang Ye | 6 | 138 | 18.73 |