Estimation of the regularisation parameter in Huber-MRF for image resolution enhancement
The Huber Markov Random Field (H-MRF) has been proposed for image resolution enhancement as a preferable alternative to Gaussian Random Markov Fields (G-MRF) for its ability to preserve discontinuities in the image. However, its performance relies on a good choice of a regularisation parameter. Whil...
Saved in:
| Main Authors: | , |
|---|---|
| 格式: | Conference Paper |
| 语言: | en_US |
| 出版: |
2015
|
| 主题: | |
| 在线阅读: | http://ddms.usim.edu.my/handle/123456789/9197 |
| 标签: |
添加标签
没有标签, 成为第一个标记此记录!
|
| 总结: | The Huber Markov Random Field (H-MRF) has been proposed for image resolution enhancement as a preferable alternative to Gaussian Random Markov Fields (G-MRF) for its ability to preserve discontinuities in the image. However, its performance relies on a good choice of a regularisation parameter. While automating this choice has been successfully tackled for G-MRF, the more sophisticated form of H-MRF makes this problem less straightforward. In this paper we develop an approximate solution to this problem, by upper-bounding the partition function of the H-MRF. We demonstrate the working and flexibility of our approach in image super-resolution experiments. © 2013 Springer-Verlag. |
|---|