EM-type algorithms for image reconstruction with background emission and Poisson noise

Authors:
Ming Yan
Affiliations:
Department of Mathematics, University of California, Los Angeles, CA
Venue:
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Year:
2011

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Abstract

Obtaining high quality images is very important in many areas of applied sciences. In this paper, we proposed general robust expectation maximization (EM)-Type algorithms for image reconstruction when the measured data is corrupted by Poisson noise. This method is separated into two steps: EM and regularization. In order to overcome the contrast reduction introduced by some regularizations, we suggested EM-Type algorithms with Bregman iteration by applying a sequence of modified EM-Type algorithms. The numerical experiments show the effectiveness of these methods in different applications.