Abstract
The framework is presented of Bayesian image restoration for multi-valued images by means of the Q-Ising model. Hyperparameters in the probabilistic model are determined so as to maximize the marginal likelihood. Practical algorithms are described based the conventional mean-field approximation and loopy belief propagation. We compare the results empirically with those provided by conventional filters and the new methods are found to be superior.
| Original language | English |
|---|---|
| Pages (from-to) | 40-43 |
| Number of pages | 4 |
| Journal | Proceedings - International Conference on Pattern Recognition |
| Volume | 2 |
| Publication status | Published - 2004 Dec 17 |
| Event | Proceedings of the 17th International Conference on Pattern Recognition, ICPR 2004 - Cambridge, United Kingdom Duration: 2004 Aug 23 → 2004 Aug 26 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition