Critical to accurate reconstruction of sparse signals from low-dimensional low-photon count observations is the solution of nonlinear optimization problems that promote sparse solutions. In this paper, we explore recovering high-resolution sparse signals from low-resolution measurements corrupted by Poisson noise using a gradient-based optimization approach with non-convex regular-ization. In particular, we analyze zero-finding methods for solving the p-norm regularized minimization subproblems arising from a sequential quadratic approach. Numerical results from fluorescence molecular tomography are presented.
['Aramayis Orkusyan', 'Lasith Adhikari', 'Joanna Valenzuela', 'Roummel F. Marcia']
Analysis of p-norm regularized subproblem minimization for sparse photon-limited image recovery