Abstract
How to extract focused information as efficiently as possible is always the key issue of the fusion algorithms for multi-focus images. As an improved version of the third generation of artificial neural networks (ANN), spiking cortical model (SCM) has been proved to be an effective tool for dealing with the issues of image processing. Under the above background, a novel multi-focus image fusion algorithm based on improved SCM is proposed in this paper. Specifically, firstly, the traditional SCM is improved to be a modified version. Secondly, the concrete parameters setting is given and introduced in detail. Finally, in order to verify the effectiveness and feasibility of the proposed algorithm, several registered groups of source images are used in this paper. Experimental results demonstrate that the proposed algorithm is superior to the existing work in terms of both subjective visual presentation and objective evaluations compared with current typical ANN models.
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Acknowledgments
The authors would like to thank the anonymous reviewers and editors for their invaluable suggestions. The work was supported in part by the National Natural Science Foundations of China under Grant 61309008, 61309022, 61373116 and 61472302, in part by the Natural Science Foundation of Shannxi Province of China under Grant 2014JQ8349, and the Foundation of Science and Technology on Information Assurance Laboratory under Grant KJ-15-102.
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Kong, W., Lei, Y. (2017). Improved Spiking Cortical Model Based Algorithm for Multi-focus Image Fusion. In: Sun, Y., Lu, H., Zhang, L., Yang, J., Huang, H. (eds) Intelligence Science and Big Data Engineering. IScIDE 2017. Lecture Notes in Computer Science(), vol 10559. Springer, Cham. https://doi.org/10.1007/978-3-319-67777-4_45
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DOI: https://doi.org/10.1007/978-3-319-67777-4_45
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