Total variation regularization for nonlinear fluorescence tomography with an augmented Lagrangian splitting approach

Abstract

Fluorescence tomography is an imaging modality that seeks to reconstruct the distribution of fluorescent dyes inside a highly scattering sample from light measurements on the boundary. Using common inversion methods with L(2) penalties typically leads to smooth reconstructions, which degrades the obtainable resolution. The use of total variation (TV) regularization for the inverse model is investigated. To solve the inverse problem efficiently, an augmented Lagrange method is utilized that allows separating the Gauss-Newton minimization from the TV minimization. Results on noisy simulation data provide evidence that the reconstructed inclusions are much better localized and that their half-width measure decreases by at least 25% compared to ordinary L(2) reconstructions.