Cart (Loading....) | Create Account
Close category search window
 

Comparing maximum likelihood estimation and constrained Tikhonov-Miller 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.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
van Kempen, G.M.P. ; Pattern Recognition Group, Delft Univ. of Technol., Netherlands ; van der Voort, H.T.M. ; Bauman, J.G.J. ; Strasters, K.C.

The authors have compared the performance of the EM-MLE and ICTM restorations applied to confocal images. Both methods greatly reduce diffraction-induced distortions of confocal images. Due to their nonlinearity, both are able (partially) to restore data of missing frequencies. From the authors' experiments, it is clear that for their test objects, the EM-MLE algorithm performs much better than ICTM. The EM-MLE algorithm produces better results under all the conditions the authors tested, and with respect to all 3 performance measures (I-Divergence, MSE, GDT) the authors used. Only for high SNR conditions, the MSE performance of ICTM approaches the EM-MLE results. It must be noted that this conclusion is only valid for the type of objects the authors used in their experiments (sparse objects); it may well be that for more dense objects, the situation is different. The poor ICTM performance shows that its functional is not well suited for images distorted with Poisson noise. The authors did not find artifacts such as ringing in the results of either algorithm. The restoration results on the cylindrical objects show, however, that the EM-MLE algorithm has a tendency to reconstruct an image that is sharper and smaller than the original object. This aspect of EM-MLE should be investigated thoroughly. Greander's method of Sieves (1991) seems promising for regularizing the EM-MLE algorithm. Finally, to reduce the computational burden of ICTM and EM-MLE, methods to speed up these algorithms should be investigated more fully

Published in:

Engineering in Medicine and Biology Magazine, IEEE  (Volume:15 ,  Issue: 1 )

Date of Publication:

Jan/Feb 1996

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.