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Least-Squares Image Resizing Using Finite Differences

A. Muñoz Barrutia, T. Blu, M. Unser

IEEE Transactions on Image Processing, vol. 10, no. 9, pp. 1365-1378, September 2001.

We present an optimal spline-based algorithm for the enlargement or reduction of digital images with arbitrary (noninteger) scaling factors. This projection-based approach can be realized thanks to a new finite difference method that allows the computation of inner products with analysis functions that are B-splines of any degree n. A noteworthy property of the algorithm is that the computational complexity per pixel does not depend on the scaling factor a. For a given choice of basis functions, the results of our method are consistently better than those of the standard interpolation procedure; the present scheme achieves a reduction of artifacts such as aliasing and blocking and a significant improvement of the signal-to-noise ratio. The method can be generalized to include other classes of piecewise polynomial functions, expressed as linear combinations of B-splines and their derivatives.

AUTHOR="Mu{\~{n}}oz Barrutia, A. and Blu, T. and Unser, M.",
TITLE="Least-Squares Image Resizing Using Finite Differences",
JOURNAL="{IEEE} Transactions on Image Processing",

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