Jump Sparse Recovery Using the Potts Model
Martin Storath, EPFL STI LIB

Seminar • 10 February 2014 • BM 4.233

Abstract
We recover jump-sparse signals from blurred incomplete data corrupted by (possibly non-Gaussian) noise using inverse Potts energy functionals. We obtain analytical results (existence of minimizers, complexity) on inverse Potts functionals and provide relations to sparsity problems. We then propose a new optimization method for these functionals which is based on dynamic programming and the alternating direction method of multipliers (ADMM). A series of experiments shows that the proposed method yields very satisfactory jump-sparse reconstructions.