Splines: A perfect fit for signal processing

Splines, which were invented by Schoenberg more than fifty years ago [1], constitute an elegant framework for dealing with interpolation and discretization problems. They are widely used in computer-aided design and computer graphics, but have been neglected in signal and image processing applications, mostly as a consequence of what I call the “bad press” phenomenon. Thanks to some recent research efforts in signal processing and wavelet-related techniques, the virtues of splines have been revived in our community [2] — there is now compelling evidence (several independent studies [3-5]) that splines offer the best cost-performance tradeoff among available interpolation methods.