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.
This work was supported by the Center for Domain-Specific Computing (CDSC) under the NSF Expeditions in Computing Award CCF-0926127.
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Yan, M. (2011). EM-Type Algorithms for Image Reconstruction with Background Emission and Poisson Noise. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2011. Lecture Notes in Computer Science, vol 6938. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24028-7_4
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DOI: https://doi.org/10.1007/978-3-642-24028-7_4
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