Campus Units

Electrical and Computer Engineering

Document Type

Conference Proceeding

Publication Version

Accepted Manuscript

Publication Date

2015

Journal or Book Title

Proceedings of the Asilomar Conference on Signals, Systems and Computers

Conference Title

Asilomar Conference on Signals, Systems and Computers

Conference Date

November 8–11, 2015

City

Pacific Grove, CA, United States

Abstract

We develop a projected Nesterov’s proximalgradient (PNPG) scheme for reconstructing sparse signals from compressive Poisson-distributed measurements with the mean signal intensity that follows an affine model with known intercept. The objective function to be minimized is a sum of convex data fidelity (negative log-likelihood (NLL)) and regularization terms. We apply sparse signal regularization where the signal belongs to a nonempty closed convex set within the domain of the NLL and signal sparsity is imposed using total-variation (TV) penalty. We present analytical upper bounds on the regularization tuning constant. The proposed PNPG method employs projected Nesterov’s acceleration step, function restart, and an adaptive stepsize selection scheme that accounts for varying local Lipschitz constant of the NLL.We establish O k2 convergence of the PNPG method with step-size backtracking only and no restart. Numerical examples compare PNPG with the state-of-the-art sparse Poisson-intensity reconstruction algorithm (SPIRAL).

Comments

This is the accepted manuscript of a proceeding published in Proc. Asilomar Conf. Signals, Syst. Comput., Pacific Grove, CA, Nov. 2015, in press.

Copyright Owner

IEEE

Language

en

File Format

application/pdf

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