A stochastic minimum-norm approach to image and texture interpolation
Hagai Kirshner, Moshe Porat, Michael A. Unser · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · 2010
We introduce an exponential-based consistent approach to image scaling. Our model stems from Sobolev repro-ducing kernels, motivated by their role in continuous-domain stochastic autoregressive processes. The pro-posed approach imposes consistency and applies the minimum-norm criterion for determining the scaled im-age. 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 conclu-sion is that the proposed Sobolev-based image model-ing could be instrumental and a preferred alternative in major image processing tasks. 1.