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|a eng
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1 |
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|a Kirshner, Hagai
|e verfasserin
|4 aut
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| 245 |
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|a Adaptive image resizing based on continuous-domain stochastic modeling
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|c 2014
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|a Text
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 23.09.2014
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| 500 |
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|a Date Revised 31.01.2014
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|a published: Print
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|a Citation Status MEDLINE
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| 520 |
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|a We introduce an adaptive continuous-domain modeling approach to texture and natural images. The continuous-domain image is assumed to be a smooth function, and we embed it in a parameterized Sobolev space. We point out a link between Sobolev spaces and stochastic auto-regressive models, and exploit it for optimally choosing Sobolev parameters from available pixel values. To this aim, we use exact continuous-to-discrete mapping of the auto-regressive model that is based on symmetric exponential splines. The mapping is computationally efficient, and we exploit it for maximizing an approximated Gaussian likelihood function.We account for non-Gaussian Lévy-type processes by deriving a more robust estimator that is based on the sample auto-correlation sequence. Both estimators use multiple initialization values for overcoming the local minima structure of the fitting criteria. Experimental image resizing results indicate that the auto-correlation criterion can cope better with non-Gaussian processes and model mismatch. Our work demonstrates the importance of the auto-correlation function in adaptive image interpolation and image modeling tasks, and we believe it is instrumental in other image processing tasks as well
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|a Journal Article
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|a Research Support, Non-U.S. Gov't
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| 700 |
1 |
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|a Bourquard, Aurélien
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ward, John Paul
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Porat, Moshe
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Unser, Michael
|e verfasserin
|4 aut
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 23(2014), 1 vom: 20. Jan., Seite 413-23
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|g month:01
|g pages:413-23
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