Image Filtering Based on Evolutionary Algorithms

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General introduction Chapter I: Background on Evolutionary Algorithms Introduction Evolutionary algorithms Genetic algorithm Overview of Genetic Algorithms Representation and evaluation Selection Crossover operators Mutation operators Survivor Selection Evolution strategy Genetic programming Swarm intelligence Termination Condition Other evolutionary algorithms Evolutionary programming Conclusion Chapter II: Noise removal and restoration Introduction Literature review Satellite images Medical images General images Noise Models Image denoising by minimizing total variation Image denoising based on optimization algorithm Conclusion Chapter III: Effective Hybrid Genetic algorithm -proposed algorithm- Introduction Preliminaries and EHGA Algorithm Initialization Fitness Evaluation Parent Selection Crossovers Single-point Two-point Cross Grid Pixel-by-pixel random Mutation Update the initial population Check termination criterion Metrics to Measure the Quality of Images Peak signal to noise ratio Structural similarity index metric Image enhancement factor Universal quality index Visual Information Fidelity Mean Structural Similarity Conclusion Chapter IV: Applications, results and Discussions Introduction Materials and methods Image database Configuration set of the algorithm EHGA Caparisons EHGA with methods in the literature Testing EHGA to suppress noise in medical images Conclusion General Conclusion

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