Publication:
Estimation of the Regularisation Parameter in Huber-MRF for Image Resolution Enhancement

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dc.ConferencedateOCT 20-23, 2013
dc.ConferencelocationHefei, PEOPLES R CHINA
dc.Conferencename14th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL)
dc.contributor.authorPitchay, SAen_US
dc.contributor.authorKaban, Aen_US
dc.date.accessioned2024-05-29T02:49:31Z
dc.date.available2024-05-29T02:49:31Z
dc.date.issued2013
dc.description.abstractThe 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.
dc.identifier.epage301
dc.identifier.issn0302-9743
dc.identifier.scopusWOS:000329908900036
dc.identifier.spage294
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/10860
dc.identifier.volume8206
dc.languageEnglish
dc.language.isoen_US
dc.publisherSpringeren_US
dc.relation.ispartofIntelligent Data Engineering And Automated Learning - Ideal 2013
dc.sourceWeb Of Science (ISI)
dc.subjectHuber prioren_US
dc.subjectHyper-parameter Optimisationen_US
dc.subjectSuper-resolutionen_US
dc.titleEstimation of the Regularisation Parameter in Huber-MRF for Image Resolution Enhancement
dc.typeProceedings Paperen_US
dspace.entity.typePublication

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