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| Title | A Note on Tree Model-Based Compressed Sensing Based on Augmented Lagrangian Method (in Japanese) |
|---|---|
| Authors | Shunsuke Horii 、Tota Suko 、Toshiyasu Matsushima |
| Released Year | 2012 |
| Format | Conference |
| Category | Others |
| Jounal Name | Proceedings of the 35th Symposium on Information Theory and its Applications |
| Jounal Page | vol.1, pp.320-325 |
| Published Year | 2012 |
| Published Month | 12 |
| Abstract （English） |
In this paper, we develop an efficient tree-model based compressed sensing (CS) algorithm. Development of efficient algorithms for sparse signal reconstruction problem is an important research issue and various algorithms have been proposed. Most of state-of-the-art CS algorithms assume only simple sparsity of the original signal. But a realistic signal often has further structure in itself and it is known that it is possible to develop a CS algorithm with better performance by exploiting the signal structure. Those algorithms are called model-based compressed sensing algorithm. Model-based compressed sensing algorithms are less well studied. We propose a tree-model based CS algorithm based on augmented Lagrangian method. Numerical simulation show that the proposed algorithm is favorable for some cases. |
| Note （English） |
1 |
| Manuscript | |
| Presentation |
