Comparison of simulated annealing algorithms for image restoration

J.L. Lamotte and R. Alt

Mathematics and Computers in Simulation (MATCOM), 1994, vol. 37, issue 1, 1-15

Abstract: This paper presents a comparative study of four optimisation algorithms based on simulated annealing: the Gibbs Sampler, the Metropolis algorithm, the Iterated Conditional Modes, and an original method of random descent proposed by the authors. Comparison criteria that have been chosen are the convergence speed, the quality of the optimum obtained with a new specific energy function and the total computing time necessary for image restoration. The four algorithms are applied to the special case of restoration of images disturbed by a gaussian white noise. Moreover, a new function to be minimized and several efficient heuristics allowing to decrease computing time are proposed. Some numerical experiments are given.

Keywords: Simulated annealing; Images restoration; Energy function (search for similar items in EconPapers)
Date: 1994
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/037847549490054X
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:37:y:1994:i:1:p:1-15

DOI: 10.1016/0378-4754(94)90054-X

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().