Genetic algorithm for maximum entropy image restoration

Cristian E. Toma; Mihai P. Datcu

doi:10.1117/12.179235

30 June 1994 Genetic algorithm for maximum entropy image restoration

Cristian E. Toma, Mihai P. Datcu

Proceedings Volume 2304, Neural and Stochastic Methods in Image and Signal Processing III; (1994) https://doi.org/10.1117/12.179235
Event: SPIE's 1994 International Symposium on Optics, Imaging, and Instrumentation, 1994, San Diego, CA, United States

Abstract

Linear image restoration techniques induce erroneous detail around sharp intensity changes. Thus, considerable work has centered on nonlinear methods, which incorporate constraints to reduce the artifacts generated in the restoration. In our paper, we examine the applicability of genetic algorithms to solving optimization problems posed by nonlinear image recovery techniques, particularly by maximum entropy restoration. Each point in the solution space is a feasible image, with the pixels as decision variables. Search is multiobjective: the entropy of the estimate must be maximized, subject to constraints dependent on the observed data and image degradation model. We use Pareto techniques to achieve this combined requirement, and problem-oriented knowledge to direct the search. Typical issues for genetic algorithms are addressed: chromosomal representation, genetic operators, selection scheme, and initialization.

Citation Download Citation

Cristian E. Toma and Mihai P. Datcu "Genetic algorithm for maximum entropy image restoration", Proc. SPIE 2304, Neural and Stochastic Methods in Image and Signal Processing III, (30 June 1994); https://doi.org/10.1117/12.179235

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available