Inverse problems with L¹ data fitting
Christian Clason, Bangti Jin, and Karl Kunisch
For non-Gaussian noise models such as impulsive noise, e.g. salt-and-pepper or random-valued noise, L¹ data fitting is more robust than standard L² terms, but leads to non-differentiable functionals to be minimized. The goal is to develop superlinearly convergent numerical methods for such inverse problems, including the automatic choice of regularization parameters.
