ICASSP 2016accepted0 citations
Analysis of p-norm regularized subproblem minimization for sparse photon-limited image recovery
Aramayis Orkusyan, Lasith Adhikari, Joanna Valenzuela, Roummel F. Marcia
Abstract
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.
BibTeX
@inproceedings{icassp2016_analysisofpnormr,
title = {Analysis of p-norm regularized subproblem minimization for sparse photon-limited image recovery},
author = {Aramayis Orkusyan and Lasith Adhikari and Joanna Valenzuela and Roummel F. Marcia},
booktitle = {ICASSP 2016},
year = {2016}
}