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A Stochastic Minimum-Norm Approach to Image and Texture Interpolation

H. Kirshner, M. Porat, M. Unser

Proceedings of the Eighteenth European Signal Processing Conference (EUSIPCO'10), Ålborg, Kingdom of Denmark, August 23-27, 2010, pp. 1004-1008.


We introduce an exponential-based consistent approach to image scaling. Our model stems from Sobolev reproducing kernels, motivated by their role in continuous-domain stochastic autoregressive processes. The proposed approach imposes consistency and applies the minimum-norm criterion for determining the scaled image. We show by experimental results that the proposed approach provides images that are visually better than other consistent solutions. We also observe that the proposed exponential kernels yield better interpolation results than polynomial B-spline models. Our conclusion is that the proposed Sobolev-based image modeling could be instrumental and a preferred alternative in major image processing tasks.

@INPROCEEDINGS(http://bigwww.epfl.ch/publications/kirshner1001.html,
AUTHOR="Kirshner, H. and Porat, M. and Unser, M.",
TITLE="A Stochastic Minimum-Norm Approach to Image and Texture
	Interpolation",
BOOKTITLE="Proceedings of the Eighteenth European Signal Processing
	Conference ({EUSIPCO'10})",
YEAR="2010",
editor="",
volume="",
series="",
pages="1004--1008",
address="{\AA}lborg, Kingdom of Denmark",
month="August 23-27,",
organization="",
publisher="",
note="")
© 2010 EURASIP. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from EURASIP. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
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