A Stochastic Minimum-Norm Approach to Image and Texture Interpolation

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.


Published in:
Proceedings of the Eighteenth European Signal Processing Conference (EUSIPCO'10), Ålborg, Denmark, 1004–1008
Year:
2010
Publisher:
EURASIP
Laboratories:




 Record created 2015-09-18, last modified 2018-03-17

External links:
Download fulltextURL
Download fulltextURL
Download fulltextURL
Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)