Pitchay, SASAPitchayKaban, AAKaban2024-05-292024-05-2920130302-9743WOS:000329908900036https://oarep.usim.edu.my/handle/123456789/10860The 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.en-USHuber priorHyper-parameter OptimisationSuper-resolutionProceedings Paper2943018206