Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel single image Bayesian super-resolution (SR) algorithm where the hyperspectral image (HSI) is the only source of information. The main contribution of the proposed approach is to convert the ill-posed SR reconstruction problem in the spectral domain to a quadratic optimization problem in the abundance map domain. In order to do so, Markov random field based energy ... |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/TIP.2018.2814210 | IEEE Transactions on Image Processing |
Keywords | Field | DocType |
Hyperspectral imaging,Image reconstruction,Minimization,Spatial resolution,Bayes methods,Dictionaries | Iterative reconstruction,Peak signal-to-noise ratio,Pattern recognition,Markov random field,Hyperspectral imaging,Augmented Lagrangian method,Artificial intelligence,Maximum a posteriori estimation,Quadratic programming,Image resolution,Mathematics | Journal |
Volume | Issue | ISSN |
27 | 6 | 1057-7149 |
Citations | PageRank | References |
6 | 0.43 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hasan Irmak | 1 | 6 | 1.78 |
| Gozde Bozdagi Akar | 2 | 129 | 20.15 |
| Seniha Esen Yuksel | 3 | 100 | 10.22 |