The L-1-Potts Functional For Robust Jump-Sparse Reconstruction
We investigate the nonsmooth and nonconvex L-1-Potts functional in discrete and continuous time. We show Gamma-convergence of discrete L-1-Potts functionals toward their continuous counterpart and obtain a convergence statement for the corresponding minimizers as the discretization gets finer. For the discrete L-1-Potts problem, we introduce an O(n(2)) time and O(n) space algorithm to compute an exact minimizer. We apply L-1-Potts minimization to the problem of recovering piecewise constant signals from noisy measurements f. It turns out that the L-1-Potts functional has a quite interesting blind deconvolution property. In fact, we show that mildly blurred jump-sparse signals are reconstructed by minimizing the L-1-Potts functional. Furthermore, for strongly blurred signals and a known blurring operator, we derive an iterative reconstruction algorithm.