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Analysis of p-norm Regularized Subproblem Minimization for Sparse Photon-Limited Image Recovery

Citation Author(s):
Aramayis Orkusyan, Lasith Adhikari, Joanna Valenzuela, Roummel F. Marcia
Submitted by:
Lasith Adhikari
Last updated:
16 March 2016 - 2:44pm
Document Type:
Poster
Document Year:
2016
Event:
Presenters:
Lasith Adhikari
 

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 work, we explore recovering high-resolution sparse signals from low-resolution measurements corrupted by Poisson noise using a gradient-based optimization approach with non-convex regularization. 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.

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