Abstract
Most of the techniques for image restoration are based on some known degradation models. Here a genetic algorithm based filter is used to restore the degraded image without having any prior knowledge about the blurring model or noise type. The observed degraded image is denoised and the initial target image is generated by blind deconvolution technique using higher-order statistics. Recombination and mutation mechanisms are implemented to create better individuals. More good solutions are generated by the selection of fittest individuals. The selection procedure is based on the similarity of the individuals with the target image. In this method, the initial target image is obtained by significantly removing noise with both Gaussian and non-Gaussian probability distributions, hence the convergence of the solution set becomes faster.
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Krishnan, N., Muthukumar, S., Ravi, S., Shashikala, D., Pasupathi, P. (2013). Image Restoration by Using Evolutionary Technique to Denoise Gaussian and Impulse Noise. In: Prasath, R., Kathirvalavakumar, T. (eds) Mining Intelligence and Knowledge Exploration. Lecture Notes in Computer Science(), vol 8284. Springer, Cham. https://doi.org/10.1007/978-3-319-03844-5_31
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DOI: https://doi.org/10.1007/978-3-319-03844-5_31
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03843-8
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