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30 June 1994 Genetic algorithm for maximum entropy image restoration
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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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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