Name
Abstract | ||
|---|---|---|
Superresolution is an image processing technique that estimates an original high-resolution image from its low-resolution
and degraded observations. In superresolution tasks, there have been problems regarding the computational cost for the estimation
of high-dimensional variables. These problems are now being overcome by the recent development of fast computers and the development
of powerful computational techniques such as variational Bayesian approximation. This paper reviews a Bayesian treatment of
the superresolution problem and presents its extensions based on hierarchical modeling by employing hidden variables. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1007/s11424-010-9277-0 | J. Systems Science & Complexity |
Keywords | Field | DocType |
hidden variables,bayesian estimation,variational estimation.,markov random fields,variational estimation,image superresolution,hierarchical model,low resolution | Hierarchical modeling,Pattern recognition,Image processing,Artificial intelligence,Hidden variable theory,Superresolution,Bayes estimator,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
23 | 1 | 1559-7067 |
Citations | PageRank | References |
0 | 0.34 | 16 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Atsunori Kanemura | 1 | 75 | 12.78 |
| Shin-ichi Maeda | 2 | 26 | 8.11 |
| Wataru Fukuda | 3 | 5 | 1.49 |
| Shin Ishii | 4 | 239 | 34.39 |