Maximum-Entropy Expectation-Maximization Algorithm for Image Reconstruction and Sensor Field Estimation
In this paper, we propose a maximum-entropy expectation-maximization (MEEM) algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed for smoothness of the estimated density function. The derivation of the MEEM algorithm requires determination of the...
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Published in | IEEE transactions on image processing Vol. 17; no. 6; pp. 897 - 907 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
New York, NY
IEEE
01.06.2008
Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subjects | |
Online Access | Get full text |
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Summary: | In this paper, we propose a maximum-entropy expectation-maximization (MEEM) algorithm. We use the proposed algorithm for density estimation. The maximum-entropy constraint is imposed for smoothness of the estimated density function. The derivation of the MEEM algorithm requires determination of the covariance matrix in the framework of the maximum-entropy likelihood function, which is difficult to solve analytically. We, therefore, derive the MEEM algorithm by optimizing a lower-bound of the maximum-entropy likelihood function. We note that the classical expectation-maximization (EM) algorithm has been employed previously for 2-D density estimation. We propose to extend the use of the classical EM algorithm for image recovery from randomly sampled data and sensor field estimation from randomly scattered sensor networks. We further propose to use our approach in density estimation, image recovery and sensor field estimation. Computer simulation experiments are used to demonstrate the superior performance of the proposed MEEM algorithm in comparison to existing methods. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Article-2 ObjectType-Feature-1 |
ISSN: | 1057-7149 1941-0042 |
DOI: | 10.1109/TIP.2008.921996 |