Image Modeling and Analysis

Maximum entropy Kalman filter for image reconstruction and compression

[+] Author Affiliations
Nastooh Avesta

University of Turku, Laboratory of Electronics and Information Technology, Department of Information Technology, FIN-20014 Turku, Finland E-mail: nastooh.avessta@utu.fi

Tyseer Aboulnasr

University of Ottawa, CASP Laboratory, School of Information Technology Engineering 800 King Edward Avenue K1N-GN5 Ottawa Canada E-mail: aboulnsar@site.uottawa.ca

J. Electron. Imaging. 13(4), 738-755 (Oct 01, 2004). doi:10.1117/1.1789135
History: Received May 6, 2002; Revised Nov. 19, 2002; Revised Jan. 24, 2003; Revised Feb. 24, 2004; Accepted Feb. 26, 2004; Online September 30, 2004
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Adaptive processes often attempt to minimize the mean square error (MSE) to filter a partially observed digital signal. While mathematically tractable, the MSE criterion often causes oversmoothing of the filtered signal. In this paper, we propose using maximum entropy (ME) as the optimization criterion to avoid the oversmoothing of signals. This criterion is motivated by the fact that ME methods make no assumptions regarding the unobserved data, aside from explicitly stated ones. The maximum entropy Kalman filter presented in this paper employs ME as its optimization criterion to explicitly identify the appropriate parameters of the standard Kalman filter, for the purpose of image compression and reconstruction. © 2004 SPIE and IS&T.

© 2004 SPIE and IS&T

Citation

Nastooh Avesta and Tyseer Aboulnasr
"Maximum entropy Kalman filter for image reconstruction and compression", J. Electron. Imaging. 13(4), 738-755 (Oct 01, 2004). ; http://dx.doi.org/10.1117/1.1789135


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