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...
Saved in:
Published in | 2009 16th IEEE International Conference on Image Processing (ICIP) pp. 341 - 344 |
---|---|
Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.11.2009
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
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 |