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On texture and image interpolation using Markov models / Shira Nemirovsky in Signal processing. Image communication, Vol. 24 N° 3 (Mars 2009) 

Public ISBD [article]  in Signal processing. Image communication > Vol. 24 N° 3 (Mars 2009) . - pp. 139-157 Titre : On texture and image interpolation using Markov models Type de document : texte imprimé Auteurs : Shira Nemirovsky, Auteur ; Moshe Porat, Auteur Article en page(s) : pp. 139-157 Note générale : Electronique Langues : Anglais (eng) Mots-clés : Markov  models  Wide-sense  Markov  Autocorrelation  function  Sampling  High  resolution  Low  resolution  Texture  interpolation  Fidelity  criterion  for  texture  reconstruction  Subjective  forced-choice  test Index. décimale : 621.382 Dispositifs électroniques utilisant les effets des corps solides. Dispositifs semi-conducteurs Résumé : Markov-type models characterize the correlation among neighboring pixels in an image in many image processing applications. Specifically, a wide-sense Markov model, which is defined in terms of minimum linear mean-square error estimates, is applicable to image restoration, image compression, and texture classification and segmentation. In this work, we address first-order (auto-regressive) wide-sense Markov images with a separable autocorrelation function. We explore the effect of sampling in such images on their statistical features, such as histogram and the autocorrelation function. We show that the first-order wide-sense Markov property is preserved, and use this result to prove that, under mild conditions, the histogram of images that obey this model is invariant under sampling. Furthermore, we develop relations between the statistics of the image and its sampled version, in terms of moments and generating model noise characteristics. Motivated by these results, we propose a new method for texture interpolation, based on an orthogonal decomposition model for textures. In addition, we develop a novel fidelity criterion for texture reconstruction, which is based on the decomposition of an image texture into its deterministic and stochastic components. Experiments with natural texture images, as well as a subjective forced-choice test, demonstrate the advantages of the proposed interpolation method over presently available interpolation methods, both in terms of visual appearance and in terms of our novel fidelity criterion. DEWEY : 361.382 ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235640%23 [...] [article] On texture and image interpolation using Markov models [texte imprimé] / Shira Nemirovsky, Auteur ; Moshe Porat, Auteur . - pp. 139-157. Electronique Langues : Anglais (eng) in Signal processing. Image communication > Vol. 24 N° 3 (Mars 2009) . - pp. 139-157 Mots-clés : Markov  models  Wide-sense  Markov  Autocorrelation  function  Sampling  High  resolution  Low  resolution  Texture  interpolation  Fidelity  criterion  for  texture  reconstruction  Subjective  forced-choice  test Index. décimale : 621.382 Dispositifs électroniques utilisant les effets des corps solides. Dispositifs semi-conducteurs Résumé : Markov-type models characterize the correlation among neighboring pixels in an image in many image processing applications. Specifically, a wide-sense Markov model, which is defined in terms of minimum linear mean-square error estimates, is applicable to image restoration, image compression, and texture classification and segmentation. In this work, we address first-order (auto-regressive) wide-sense Markov images with a separable autocorrelation function. We explore the effect of sampling in such images on their statistical features, such as histogram and the autocorrelation function. We show that the first-order wide-sense Markov property is preserved, and use this result to prove that, under mild conditions, the histogram of images that obey this model is invariant under sampling. Furthermore, we develop relations between the statistics of the image and its sampled version, in terms of moments and generating model noise characteristics. Motivated by these results, we propose a new method for texture interpolation, based on an orthogonal decomposition model for textures. In addition, we develop a novel fidelity criterion for texture reconstruction, which is based on the decomposition of an image texture into its deterministic and stochastic components. Experiments with natural texture images, as well as a subjective forced-choice test, demonstrate the advantages of the proposed interpolation method over presently available interpolation methods, both in terms of visual appearance and in terms of our novel fidelity criterion. DEWEY : 361.382 ISSN : 0923-5965 En ligne : http://www.sciencedirect.com/science?_ob=PublicationURL&_tockey=%23TOC%235640%23 [...]