Polynomial preserving algorithm for digital image interpolation

Abstract Interpolation is a process of estimating the intermediate values of the samples from their neighbouring points. The original samples and the new interpolation values can be regarded as realization of a given signal at different resolution levels. An interpolation algorithm is therefore served as a link between different resolution levels of the signal. In this paper, a family of iterative interpolation algorithm is introduced. The algorithm uses splines iteratively and preserves certain polynomials. Comparison with cubic convolution, cubic spline, Daubechies’ wavelet and FFT-based interpolations is made. The tensor product of two one-dimensional interpolations is applied to digital images.

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