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Analysis of p-norm Regularized Subproblem Minimization for Sparse Photon-Limited Image Recovery
- Citation Author(s):
- Submitted by:
- Lasith Adhikari
- Last updated:
- 16 March 2016 - 2:44pm
- Document Type:
- Poster
- Document Year:
- 2016
- Event:
- Presenters:
- Lasith Adhikari
- Categories:
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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.
ICASSP2016.pdf
ICASSP2016.pdf (577)