By Topic

Relaxation algorithms for MAP estimation of gray-level images with multiplicative noise

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

3 Author(s)
Simchony, T. ; Signal & Image Processing Inst., Univ. of Southern California, Los Angeles, CA, USA ; Chellappa, R. ; Lichtenstein, Z.

The authors present a comparison between stochastic and deterministic relaxation algorithms for maximum a posteriori estimation of gray-level images modeled by noncausal Gauss-Markov random fields (GMRF) and corrupted by film grain noise. The degradation involves nonlinear transformation and multiplicative noise. Parameters for the GMRF model were estimated from the original image using maximum-likelihood techniques. To overcome modeling errors, a constraint minimization approach is suggested for estimating the parameters to ensure the positivity of the power spectral density function. Real image experiments with various noise variances and magnitudes of the nonlinear transformation are presented

Published in:

Information Theory, IEEE Transactions on  (Volume:36 ,  Issue: 3 )