A stochastic model-based approach to image and texture interpolation
Hagai Kirshner, Moshe Porat · 2009
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.