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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...

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Main Author: Sakinah Ali, Pitchay,
Format: Conference Paper
Language:en_US
Published: Springer 2015
Subjects:
Huber prior
Hyper-parameter Optimisation
Super-resolution
Online Access:http://ddms.usim.edu.my/handle/123456789/8946
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spelling my.usim-89462015-08-05T02:39:36Z Estimation of the Regularisation Parameter in Huber-MRF for Image Resolution Enhancement Sakinah Ali, Pitchay, Huber prior Hyper-parameter Optimisation Super-resolution 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. 2015-08-05T02:39:36Z 2015-08-05T02:39:36Z 2013-01-01 Conference Paper 978-3-642-41278-3 978-3-642-41277-6 0302-9743 http://ddms.usim.edu.my/handle/123456789/8946 en_US Springer
institution Universiti Sains Islam Malaysia
building USIM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universit Sains Islam i Malaysia
content_source USIM Institutional Repository
url_provider http://ddms.usim.edu.my/
language en_US
topic Huber prior
Hyper-parameter Optimisation
Super-resolution
spellingShingle Huber prior
Hyper-parameter Optimisation
Super-resolution
Sakinah Ali, Pitchay,
Estimation of the Regularisation Parameter in Huber-MRF for Image Resolution Enhancement
description 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.
format Conference Paper
author Sakinah Ali, Pitchay,
author_facet Sakinah Ali, Pitchay,
author_sort Sakinah Ali, Pitchay,
title Estimation of the Regularisation Parameter in Huber-MRF for Image Resolution Enhancement
title_short Estimation of the Regularisation Parameter in Huber-MRF for Image Resolution Enhancement
title_full Estimation of the Regularisation Parameter in Huber-MRF for Image Resolution Enhancement
title_fullStr Estimation of the Regularisation Parameter in Huber-MRF for Image Resolution Enhancement
title_full_unstemmed Estimation of the Regularisation Parameter in Huber-MRF for Image Resolution Enhancement
title_sort estimation of the regularisation parameter in huber-mrf for image resolution enhancement
publisher Springer
publishDate 2015
url http://ddms.usim.edu.my/handle/123456789/8946
_version_ 1645152505984712704
score 13.214268

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