On wide-sense Markov Random fields and their application to improved texture interpolation

Shira Nemirovsky, Moshe Porat

Research output: Contribution to journalConference articlepeer-review


Random field models characterize the correlation between neighboring pixels in an image. Specifically, a widesense Markov model is obtained by assuming a separable correla-tion function for a 2D auto-regressive (AR) model. In this work we analyze the effect of sub-sampling on statistical features of an image such as histogram and the autocorrelation function. We show that the Markovian property is preserved for the 2nd-order case (of the widesense model) and use this result to prove that, under mild conditions, the histogram of such images is invariant under sub-sampling. Furthermore, we develop relations between the statistics of the image and its sub-sampled version in terms of moments and noise characteristics. Motivated by these results, we propose a new method for texture interpolation, based on orthogonal decomposition. Experiments with natural texture images demonstrate the advantages of the proposed method over presently available interpolation methods. copyright by EURASIP.

Original languageEnglish
JournalEuropean Signal Processing Conference
StatePublished - 2008
Externally publishedYes
Event16th European Signal Processing Conference, EUSIPCO 2008 - Lausanne, Switzerland
Duration: 25 Aug 200829 Aug 2008

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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