Decoupled Solution for Composite Sparse-plus-Smooth Inverse Problems

Abstract: We consider composite linear inverse problems where the signal to recover is modeled as a sum of two functions. We study a variational framework formulated as an optimization problem over the pairs of components using two regularization terms, each of them acting on a different part of the solution. The specificity of our work is to study the case where one component is regularized with an atomic norm over a Banach space, which is known to promote sparse reconstruction, while the other is regularized with a quadratic norm over a Hilbert space, which promotes smooth solution. We show how this composite optimization problem can be reduced to an optimization problem over the Banach space component only up to a linear problem. This reveals a decoupling between the two components, allowing for a new composite representer theorem. It naturally induces a decoupled numerical procedure to solve the composite optimization problem. We exemplify our main result with a composite deconvolution problem of Dirac recovery over a smooth background. In this setting, we illustrate the relevance of a composite model and show a significant temporal gain on signal reconstruction, which results from our decoupled algorithmic approach.