Abstract
Separation of sources from one-dimensional mixture signals such as speech has been largely explored. However, two-dimensional sources (images) separation problem has only been examined to a limited extent. The reason is that ICA is a very general-purpose statistical technique, and it does not take the spatial information into account while separating mixture images. In this paper, we introduce Markov random field model to incorporate the spatial information into ICA. MRF is considered as a powerful tool to model the joint probability distribution of the image pixels in terms of local spatial interactions. An MRF-ICA based algorithm is proposed for image separation. It is successfully demonstrated on artificial and real images.
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© 2005 Springer-Verlag Berlin Heidelberg
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Jia, S., Qian, Y. (2005). An MRF-ICA Based Algorithm for Image Separation. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_57
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DOI: https://doi.org/10.1007/11539117_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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