Abstract
Kernel PCA has been applied to image processing, even though, it is known to have high computational complexity. We introduce centered Subset KPCA for image denoising problems. Subset KPCA has been proposed for reduction of computational complexity of KPCA, however, it does not consider a pre-centering that is often important for image processing. Indeed, pre-centering of Subset KPCA is not straightforward because Subset KPCA utilizes two sets of samples. We propose an efficient algorithm for pre-centering, and provide an algorithm for pre-image. Experimental results show that our method is comparable with a state-of-the-art image denoising method.
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© 2011 Springer-Verlag Berlin Heidelberg
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Washizawa, Y., Tanaka, M. (2011). Centered Subset Kernel PCA for Denoising. In: Koch, R., Huang, F. (eds) Computer Vision – ACCV 2010 Workshops. ACCV 2010. Lecture Notes in Computer Science, vol 6469. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22819-3_36
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DOI: https://doi.org/10.1007/978-3-642-22819-3_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22818-6
Online ISBN: 978-3-642-22819-3
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