I will present a novel method for sparse coding for nonlinear inverse problem. It relies on a specific formulation of the optimisation problem allowing the use of the projected gradient descent (PGD) method. The main advantage of PGD over the conventional ADMM-based approach is that the costly step of inverting the forward model is avoided. Instead, we only require the computation of the gradient of the data fidelity term (without an explicit inversion of the forward model). We will discuss of the projection on the set of constraints as well as the convergence of the algorithm. Finally, I will present some preliminary results.
Steer&Detect on Images 14 Nov 2017
First steps toward fast PET reconstruction30 May 2017