Image magnification based on adaptive MRF model parameter estimation

Xiaoling Zhang, Kin Man Lam, Lansun Shen

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)

Abstract

The Markov Random Field (MRF) model, whose model parameters specify the amount of smoothness in an image, is a popular approach to image magnification. The model parameters must be estimated accurately in order to obtain an elegant solution. The conventional parameter estimation methods consider an image to be homogeneous and have a high computational complexity. However, images are usually not homogenous; using only one set of parameters cannot describe a whole image effectively. We therefore devise an adaptive parameter estimation method for the MRF model to reduce the blocky artifact while preserving the edges in the (high-resolution) HR image. In our method, an initial estimated HR image is divided into small blocks, and the respective parameters are then estimated. Their values are defined as inversely proportional to their energy in the corresponding direction. Then, the gradient descent algorithm is employed iteratively to obtain an improved HR image in a Bayesian MAP framework. Experimental results show that, when compared to the MRF model with a fixed set of parameters, using the MRF model with our adaptive parameter estimation method can produce a magnified image with the edges and texture well preserved. Both the PSNR and visual quality of our proposed method are much better than the fixed-parameter method.
Original languageEnglish
Title of host publicationProceedings of 2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005
PublisherIEEE
Pages653-656
Number of pages4
Volume2005
ISBN (Print)0780392663, 9780780392663
DOIs
Publication statusPublished - 1 Dec 2005
Event2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005 - Hong Kong, Hong Kong
Duration: 13 Dec 200516 Dec 2005

Conference

Conference2005 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2005
CountryHong Kong
CityHong Kong
Period13/12/0516/12/05

Keywords

  • Bayes methods
  • Markov processes
  • Adaptive estimation
  • Computational complexity
  • Image processing
  • Maximum likelihood estimation

ASJC Scopus subject areas

  • Engineering(all)

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