Abstract
In this paper, a motion blur kernel estimation method is proposed based on genetic algorithms. Specific individual representation and initialization are developed for optimizing motion blur kernel. Effective fitness function and genetic operators are designed to ensure the convergence of the optimization process. Several traditional methods are compared with the proposed method. Real aerial image data sets are used to test the performance of these methods. It is proved that the proposed method can estimate the blur kernel more effectively and accurately than traditional ones.
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Acknowledgment
The authors gratefully acknowledge the support to this work from all our colleagues in Bei**g Engineering Research Center of Aerial Intelligent Remote Sensing Equipment.
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Zhou, Z. et al. (2020). Accurate Estimation of Motion Blur Kernel Based on Genetic Algorithms. In: Wang, Y., Li, X., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2020. Communications in Computer and Information Science, vol 1314. Springer, Singapore. https://doi.org/10.1007/978-981-33-6033-4_4
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DOI: https://doi.org/10.1007/978-981-33-6033-4_4
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