Genetic Algorithms Based Artificial Neural Networks for Blur Identification and Restoration of Degraded Images
In this research paper, we present a n novel idea of using genetic algorithms to search global minimum of the error performance surface of a blind image restoration problems using artificial neural networks. The artificial neural network was based on autoregresseive moving average network with random Gaussian process in which the noisy and blurred images are modeled as continuos associative networks, where as auto-associative part determines the image model coefficients and the hetero-associative part determines the blur function of the system. The weights of the networks were first of all initialized using genetic algorithm after then iterative gradient based algorithm was used to minimize the error function, therefore, self-organization like structure of the proposed neural network provides the potential solution of the blind image restoration problem. The beauty of the algorithm lies in the fact that estimation and restoration are implemented simultaneously.
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