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- W2093733524 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." @default.
- W2093733524 created "2016-06-24" @default.
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- W2093733524 date "1994-06-30" @default.
- W2093733524 modified "2023-09-23" @default.
- W2093733524 title "<title>Genetic algorithm for maximum entropy image restoration</title>" @default.
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- W2093733524 doi "https://doi.org/10.1117/12.179235" @default.
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