论文信息 - Linear Methods for Image Interpolation

Linear Methods for Image Interpolation

We discuss linear methods for interpolation, including nearest neighbor, bilinear, bicubic, splines, and sinc interpolation. We focus on separable interpolation, so most of what is said applies to one-dimensional interpolation as well as N-dimensional separable interpolation. Source Code The source code (ANSI C), its documentation, and the online demo are accessible at the IPOL web page of this article 1 .

Pascal Getreuer | Pascal Getreuer

[1] M. Unser,et al. Interpolation revisited [medical images application] , 2000, IEEE Transactions on Medical Imaging.

[2] Michael Unser,et al. Splines: a perfect fit for signal and image processing , 1999, IEEE Signal Process. Mag..

[3] E. Meijering,et al. A chronology of interpolation: from ancient astronomy to modern signal and image processing , 2002, Proc. IEEE.

[4] Michael Unser,et al. Cardinal spline filters: Stability and convergence to the ideal sinc interpolator , 1992, Signal Process..

[5] Elisabeth Larsson,et al. Stable Computations with Gaussian Radial Basis Functions , 2011, SIAM J. Sci. Comput..

[6] R. Keys. Cubic convolution interpolation for digital image processing , 1981 .

[7] Sang Joon Kim,et al. A Mathematical Theory of Communication , 2006 .

[8] E. Meijering. A chronology of interpolation: from ancient astronomy to modern signal and image processing , 2002, Proc. IEEE.

[9] M. Unser. SPLINES : A PERFECT FIT FOR SIGNAL / IMAGE PROCESSING , 1999 .

[10] Alan P. Schaum. Theory and Design of Local Interpolators , 1993, CVGIP Graph. Model. Image Process..

[11] François Malgouyres,et al. Edge Direction Preserving Image Zooming: A Mathematical and Numerical Analysis , 2001, SIAM J. Numer. Anal..

[12] G. Strang,et al. A Fourier Analysis of the Finite Element Variational Method , 2011 .

[13] Thierry Blu,et al. MOMS: maximal-order interpolation of minimal support , 2001, IEEE Trans. Image Process..