On Variational Curve Smoothing and Reconstruction

In this paper we discuss and experimentally compare variational methods for curve denoising, curve smoothing and curve reconstruction problems. The methods are based on defining suitable cost functionals to be minimized, the cost being the combination of a fidelity term measuring the “distance” of a curve from the data and a smoothness term measuring the curve’s L ₁-norm or length.