A stochastic model-based approach to image and texture interpolation

We introduce a new exponential-based shift-invariant approach to image interpolation using stochastic modeling. Our model stems from Sobolev reproducing kernels of exponential type, motivated by their role in continuous-domain stochastic autoregressive processes. An algorithm based on these tools is...

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Bibliographic Details
Published in2009 16th IEEE International Conference on Image Processing (ICIP) pp. 341 - 344
Main Authors Kirshner, H., Porat, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2009
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Summary:We introduce a new exponential-based shift-invariant approach to image interpolation using stochastic modeling. Our model stems from Sobolev reproducing kernels of exponential type, motivated by their role in continuous-domain stochastic autoregressive processes. An algorithm based on these tools is developed and tested. Experimental results of image and texture scaling show that these exponential kernels outperform currently available 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.
ISBN:9781424456536
1424456533
ISSN:1522-4880
2381-8549
DOI:10.1109/ICIP.2009.5414441