Name
Abstract | ||
|---|---|---|
Estimating a vector xfrom noisy linear measurements Ax+woften requires use of prior knowledge or structural constraints on xfor accurate reconstruction. Several recent works have considered combining linear least-squares estimation with a generic or plug-indenoiser function that can be designed in a modular manner based on the prior knowledge about x. While these methods have shown excellent performance, it has been difficult to obtain rigorous performance guarantees. This work considers plug-in denoising combined with the recently-developed Vector Approximate Message Passing (VAMP) algorithm, which is itself derived via Expectation Propagation techniques. It shown that the mean squared error of thisplug-in VAMP can be exactly predicted for a large class of high-dimensional random Abfand denoisers. The method is illustrated in image reconstruction and parametric bilinear estimation. |
Year | Venue | Keywords |
|---|---|---|
2018 | NeurIPS | expectation propagation,mean squared error,structural constraints,image reconstruction,the method |
DocType | Volume | Citations |
Conference | abs/1806.10466 | 4 |
PageRank | References | Authors |
0.38 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alyson K. Fletcher | 1 | 552 | 41.10 |
| Sundeep Rangan | 2 | 3101 | 163.90 |
| Subrata Sarkar | 3 | 7 | 2.78 |
| Philip Schniter | 4 | 1620 | 93.74 |