Name
Abstract | ||
|---|---|---|
•Algorithm enables noise reduction in volumetric OCT data while preserving important morphological structures.•Algorithm outperforms state-of-the-art methods in terms of quantitative measures.•Interestingly a slightly modified version of the algorithm successfully removes noise in volumetric CT data. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.media.2018.06.002 | Medical Image Analysis |
Keywords | Field | DocType |
Spatio-temporal denoising,Variational approach,Quasi prior,ADMM | Noise reduction,Computer vision,Optical coherence tomography,Pattern recognition,Medical imaging,Quantile,Sparse image,Regularization (mathematics),Artificial intelligence,Speckle noise,Linearization,Mathematics | Journal |
Volume | ISSN | Citations |
48 | 1361-8415 | 0 |
PageRank | References | Authors |
0.34 | 13 | 10 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Franziska Schirrmacher | 1 | 5 | 5.86 |
| Thomas Köhler | 2 | 38 | 9.14 |
| Jürgen Endres | 3 | 4 | 2.42 |
| Tobias Lindenberger | 4 | 0 | 0.34 |
| Lennart Husvogt | 5 | 4 | 4.16 |
| James G. Fujimoto | 6 | 12 | 6.61 |
| Joachim Hornegger | 7 | 1734 | 190.62 |
| Arnd Dörfler | 8 | 20 | 6.06 |
| Philip Hoelter | 9 | 1 | 1.36 |
| Andreas K. Maier | 10 | 560 | 178.76 |