Image Enlargement by Nonlinear Frequency Extrapolation with Morphological Operators

A Sobolev reproducing-kernel Hilbert space approach to image interpolation is introduced. The underlying kernels are exponential functions and are related to stochastic autoregressive image modeling. The corresponding image interpolants can be implemented effectively using compactly-supported exponential B-splines. A tight l(2) upper-bound on the interpolation error is then derived, suggesting that the proposed exponential functions are optimal in this regard. Experimental results indicate that the proposed interpolation approach with properly-tuned, signal-dependent weights outperforms currently available polynomial B-spline models of comparable order. Furthermore, a unified approach to image interpolation by ideal and nonideal sampling procedures is derived, suggesting that the proposed exponential kernels may have a significant role in image modeling as well. Our conclusion is that the proposed Sobolev-based approach could be instrumental and a preferred alternative in many interpolation tasks.

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On the Role of Exponential Splines in Image Interpolation

Kirshner

Porat

2009

IEEE Trans. on Image Process.

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Image Enlargement by Nonlinear Frequency Extrapolation with Morphological Operators

Cited by 1 publication

References 10 publications

On the Role of Exponential Splines in Image Interpolation

On the Role of Exponential Splines in Image Interpolation

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