Abstract
This paper describes an application of support vector machine to pixel-level multifocus image fusion problem based on the use of spatial features of image blocks. The algorithm first decomposes the source images into blocks. Given two of these blocks (one from each source image), a SVM is trained to determine which one is clearer. Fusion then proceeds by selecting the clearer block in constructing the final image. Experimental results show that the proposed method outperforms the discrete wavelet transform based approach, particularly when there is movement in the objects or misegistration of the source images.
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© 2005 Springer-Verlag Berlin Heidelberg
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Li, S., Wang, Y. (2005). Multifocus Image Fusion Using Spatial Features and Support Vector Machine. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_121
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DOI: https://doi.org/10.1007/11427445_121
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
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