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 Author: | |
|---|---|
| Format: | Conference Paper |
| Language: | en_US |
| Published: |
Springer
2015
|
| Subjects: | |
| Online Access: | http://ddms.usim.edu.my/handle/123456789/8946 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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. |
|---|