EPFL
 Biomedical Imaging GroupSTI
EPFL
  Publications
English only   BIG > Publications > Data-Adaptive Kernel


 CONTENTS
 Home Page
 News & Events
 People
 Publications
 Tutorials and Reviews
 Research
 Demos
 Download Algorithms

 DOWNLOAD
 PDF
 Postscript
 All BibTeX References

Linear Interpolation of Biomedical Images Using a Data-Adaptive Kernel

H. Kirshner, A. Bourquard, J.P. Ward, M. Unser

Proceedings of the Tenth IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI'13), San Francisco CA, USA, April 7-11, 2013, pp. 926-929.



In this work, we propose a continuous-domain stochastic model that can be applied to image data. This model is autoregressive, and accounts for Gaussian-type as well as for non-Gaussian-type innovations. In order to estimate the corresponding parameters from the data, we introduce two possible error criteria; namely, Gaussian maximum-likelihood, and least-squares autocorrelation fit. Exploiting the link between autoregressive models and spline approximation, we use our approach to adapt interpolation parameters to a given image. Our numerical results demonstrate that our adaptive approach yields higher SNR values compared to classical polynomial splines for the task of image scaling. They also indicate that our least-squares-based error criterion nearly achieves the oracle performance for parameter estimation, which provides further support to the practical relevance of our model.


@INPROCEEDINGS(http://bigwww.epfl.ch/publications/kirshner1303.html,
AUTHOR="Kirshner, H. and Bourquard, A. and Ward, J.P. and Unser, M.",
TITLE="Linear Interpolation of Biomedical Images Using a Data-Adaptive
        Kernel",
BOOKTITLE="Proceedings of the Tenth IEEE International Symposium on
        Biomedical Imaging: {F}rom Nano to Macro ({ISBI'13})",
YEAR="2013",
editor="",
volume="",
series="",
pages="926--929",
address="San Francisco CA, USA",
month="April 7-11,",
organization="",
publisher="",
note="")

© 2013 IEEE. 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 IEEE.
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.