By Topic

A new accelerated EM based learning of the image parameters and restoration

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

The purchase and pricing options are temporarily unavailable. Please try again later.
2 Author(s)
F. Sari ; Marmara Res. Center, Information Technol. Res. Inst., Kocaeli, Turkey ; M. E. Celebi

We propose a new method based on the accelerated expectation maximization (EM) algorithm to learn the unknown image parameters and restoration. Acceleration is provided using fisher scoring (FS) optimization in the M step. Only a small number FS iteration is required for each M step. Our proposed algorithm reaches to the local minima in few steps whereas conventional EM needs more iteration. We also estimate the regularization parameter in the same single structure. Thanks to the FS optimization, it is possible to avoid complicated second derivative of the log-likelihood function by using only the gradient values.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:4 )

Date of Conference:

25-29 July 2004