Optimized Compact-Support Interpolation Kernels

Abstract—In this paper, we investigate the problem of designing compact-support interpolation kernels for a given class of signals. By using calculus of variations, we simplify the optimization problem from an nonlinear infinite dimensional problem to a linear finite dimensional case, and then find the optimum compact-support function that best approximates a given filter in the least square sense ( 2 norm). The benefit of compactsupport interpolants is the low computational complexity in the interpolation process while the optimum compact-support interpolant guarantees the highest achievable Signal to Noise Ratio (SNR). Our simulation results confirm the superior performance of the proposed kernel compared to other conventional compactsupport interpolants such as cubic spline.

• Type: Journal
• Year: 2012
• Where: TSP
• Authors: Ramtin Madani, Ali Ayremlou, Arash Amini, Farrokh Marvasti
• Keywords: Artificial Intelligence, Calculus Of Variations, Cubic Spline, Signal To Noise Ratio, TSP 2012