On the Complexity of Mumford-Shah-Type Regularization, Viewed as a Relaxed Sparsity Constraint

We show that inverse problems with a truncated quadratic regularization are NP-hard in general to solve or even approximate up to an additive error. This stands in contrast to the case corresponding to a finite-dimensional approximation to the Mumford-Shah functional, where the operator involved is the identity and for which polynomial-time solutions are known. Consequently, we confirm the infeasibility of any natural extension of the Mumford-Shah functional to general inverse problems. A connection between truncated quadratic minimization and sparsity-constrained minimization is also discussed.