Name
Abstract | ||
|---|---|---|
A tomographic reconstruction method based on Monte Carlo random searching guided by the information contained in the projections of radiographed objects is presented. In order to solve the optimization problem, a multiscale algorithm is proposed to reduce computation. The reconstruction is performed in a coarse-to-fine multigrid scale that initializes each resolution level with the reconstruction of the previous coarser level, which substantially improves the performance. The method was applied to a real case reconstructing the internal structure of a small metallic object with internal components, showing excellent results. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/s10732-007-9017-3 | J. Heuristics |
Keywords | Field | DocType |
Tomography,Image reconstruction,Monte Carlo methods,Stochastic heuristics,Multigrid | Iterative reconstruction,Monte Carlo method in statistical physics,Mathematical optimization,Monte Carlo method,Tomographic reconstruction,Quasi-Monte Carlo method,Hybrid Monte Carlo,Tomography,Monte Carlo integration,Mathematics | Journal |
Volume | Issue | ISSN |
13 | 3 | 1381-1231 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Barbuzza | 1 | 0 | 1.01 |
| M. Vénere | 2 | 15 | 1.41 |
| A. Clausse | 3 | 42 | 5.63 |