Name
Abstract | ||
|---|---|---|
The analysis model has been previously exploited as an alternative to the classical sparse synthesis model for designing image reconstruction methods. Applying a suitable analysis operator on the image of interest yields a cosparse outcome which enables us to reconstruct the image from undersampled data. In this work, we introduce additional prior in the analysis context and theoretically study the uniqueness issues in terms of analysis operators in general position and the specific 2D finite difference operator. We establish bounds on the minimum measurement numbers which are lower than those in cases without using analysis model prior. Based on the idea of iterative cosupport detection (ICD), we develop a novel image reconstruction model and an effective algorithm, achieving significantly better reconstruction performance. Simulation results on synthetic and practical magnetic resonance (MR) images are also shown to illustrate our theoretical claims. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1155/2016/7571934 | COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE |
Field | DocType | Volume |
Iterative reconstruction,Uniqueness,General position,Finite difference,Linear model,Computer science,Artificial intelligence,Operator (computer programming),Machine learning | Journal | 2016 |
ISSN | Citations | PageRank |
1748-670X | 0 | 0.34 |
References | Authors | |
27 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Yu | 1 | 6 | 7.45 |
| Huiqian Du | 2 | 4 | 1.40 |
| Fan Lam | 3 | 50 | 9.14 |
| Wenbo Mei | 4 | 6 | 1.79 |
| Liping Fang | 5 | 0 | 0.34 |