Articles

Maximum a posteriori restoration of blurred images using self-organization

[+] Author Affiliations
Cheng-Yuan Liou, Wen-Pin Tai

National Taiwan University, Department of Computer Science and Information Engineering, Taipei 10764, Taiwan

J. Electron. Imaging. 7(1), 86-93 (Jan 01, 1998). doi:10.1117/1.482628
History: Received Feb. 10, 1997; Revised July 1, 1997; Accepted Aug. 1, 1997
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Abstract

We use the “magic TV” network with the maximum a posteriori (MAP) criterion to restore a space-dependent blurred image. This network provides a unique topological invariance mechanism that facilitates the identification of such space-dependent blur. Instead of using parametric modeling of the underlying blurred image, we use this mechanism to accomplish the restoration. The restoration is reached by a self-organizing evolution in the network, where the weight matrices are adapted to approximate the blur functions. The MAP criterion is used to indicate the goodness of the approximation and to direct the evolution of the network. © 1998 SPIE and IS&T.

© 1998 SPIE and IS&T

Citation

Cheng-Yuan Liou and Wen-Pin Tai
"Maximum a posteriori restoration of blurred images using self-organization", J. Electron. Imaging. 7(1), 86-93 (Jan 01, 1998). ; http://dx.doi.org/10.1117/1.482628


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