1 October 2004 Maximum entropy Kalman filter for image reconstruction and compression
Author Affiliations +
J. of Electronic Imaging, 13(4), (2004). doi:10.1117/1.1789135
Abstract
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.
Nastooh Avesta, Tyseer Aboulnasr, "Maximum entropy Kalman filter for image reconstruction and compression," Journal of Electronic Imaging 13(4), (1 October 2004). http://dx.doi.org/10.1117/1.1789135
JOURNAL ARTICLE
18 PAGES


SHARE
KEYWORDS
Filtering (signal processing)

Image compression

Image segmentation

Autoregressive models

Image restoration

Electronic filtering

Signal processing

RELATED CONTENT

3D watermarking scheme in stereo vision system
Proceedings of SPIE (September 16 2005)
CMOS linear array of BDJ color detectors
Proceedings of SPIE (September 07 1998)
Mechanical properties of metal honeycomb sandwich panel
Proceedings of SPIE (August 25 2009)

Back to Top