Estimation of the regularisation parameter in Huber-MRF for image resolution enhancement

• Main Authors: S., Ali Pitchay,, A., Kabán,
• Format: Conference Paper
• Language: en_US
• Published: 2015
• Subjects: Huber prior; Hyper-parameter Optimisation; Super-resolution
• Online Access: http://ddms.usim.edu.my/handle/123456789/9197

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.