Abstract

In the field of imaging polarimetry Stokes parameters are sought and must be inferred from noisy and blurred intensity measurements. Using a penalized-likelihood estimation framework we investigate reconstruction quality when estimating intensity images and then transforming to Stokes parameters, and when estimating Stokes parameters directly. We define our cost function for reconstruction by a weighted least-squares data fit term and a regularization penalty. We show that for quadratic regularization the estimators of Stokes and intensity images can be made equal by appropriate choice of regularization parameters. It is empirically shown that, when using edge preserving regularization, estimating the Stokes parameters directly leads to lower RMS error. Also, the addition of a cross channel regularization term further lowers the RMS error for both methods, especially in the case of low SNR.

© 2009 Optical Society of America

» View Full Text: Acrobat PDF (503 KB)

History
Original Manuscript: August 6, 2008
Revised Manuscript: November 25, 2008
Manuscript Accepted: January 19, 2009
Published: March 24, 2009

References

Author Affiliations

John R. Valenzuela, Jeffrey A. Fessler

University of Michigan

Cited By

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.