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    <p>This document will get you a good insight into warping: <a href="http://www.gson.org/thesis/warping-thesis.pdf" rel="nofollow">http://www.gson.org/thesis/warping-thesis.pdf</a></p> <p>However, this will include filtering out high frequencies, which will make the implementation a lot more complicated but will give a better result.</p> <p>An easy way to accomplish what you want to do would be to loop through every pixel in your final image, plug the coordinates into your splines and retrieve the pixel in your original image. This pixel might have coordinates 0.4/1.2 so you could bilinearly interpolate between 0/1, 1/1, 0/2 and 1/2.</p> <p>As for splines: there are many resources and solutions online for the 1D case. As for 2D it gets a bit trickier to find helpful resources. A simple example for the 1D case: <a href="http://www-users.cselabs.umn.edu/classes/Spring-2009/csci2031/quad_spline.pdf" rel="nofollow">http://www-users.cselabs.umn.edu/classes/Spring-2009/csci2031/quad_spline.pdf</a></p> <p>Here's a great guide for the 2D case: <a href="http://en.wikipedia.org/wiki/Bicubic_interpolation" rel="nofollow">http://en.wikipedia.org/wiki/Bicubic_interpolation</a></p> <p>Based upon this you could derive an own scheme for splines for the 2D case. Define a bivariate (with x and y) polynomial and set your constraints to solve for the coefficients of the polynomial. Just keep in mind that the borders of the spline patches have to be consistent (both in value and derivative) to avoid ugly jumps.</p> <p>Good luck!</p>
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