Halftone image resampling by interpolation and error-diffusion
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Fuzzy-adapted linear interpolation algorithm for image zooming
Signal Processing
Locally edge-adapted distance for image interpolation based on genetic fuzzy system
Expert Systems with Applications: An International Journal
Two-stage interpolation algorithm based on fuzzy logics and edges features for image zooming
EURASIP Journal on Advances in Signal Processing
Face transformation with harmonic models by the finite-volume method with delaunay triangulation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Research on Interpolation Methods in Medical Image Processing
Journal of Medical Systems
Robust impulse-noise filtering for biomedical images using numerical interpolation
ICIAR'12 Proceedings of the 9th international conference on Image Analysis and Recognition - Volume Part II
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Cubic convolution is a popular method for image interpolation. Traditionally, the piecewise-cubic kernel has been derived in one dimension with one parameter and applied to two-dimensional (2-D) images in a separable fashion. However, images typically are statistically nonseparable, which motivates this investigation of nonseparable cubic convolution. This paper derives two new nonseparable, 2-D cubic-convolution kernels. The first kernel, with three parameters (designated 2D-3PCC), is the most general 2-D, piecewise-cubic interpolator defined on [-2,2]×[-2,2] with constraints for biaxial symmetry, diagonal (or 90° rotational) symmetry, continuity, and smoothness. The second kernel, with five parameters (designated 2D-5PCC), relaxes the constraint of diagonal symmetry, based on the observation that many images have rotationally asymmetric statistical properties. This paper also develops a closed-form solution for determining the optimal parameter values for parametric cubic-convolution kernels with respect to ensembles of scenes characterized by autocorrelation (or power spectrum). This solution establishes a practical foundation for adaptive interpolation based on local autocorrelation estimates. Quantitative fidelity analyses and visual experiments indicate that these new methods can outperform several popular interpolation methods. An analysis of the error budgets for reconstruction error associated with blurring and aliasing illustrates that the methods improve interpolation fidelity for images with aliased components. For images with little or no aliasing, the methods yield results similar to other popular methods. Both 2D-3PCC and 2D-5PCC are low-order polynomials with small spatial support and so are easy to implement and efficient to apply.