An MRF-ICA based algorithm for image separation

Authors:
Sen Jia;Yuntao Qian
Affiliations:
College of Computer Science, Zhejiang University, Hangzhou, P.R. China;College of Computer Science, Zhejiang University, Hangzhou, P.R. China
Venue:
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Year:
2005

Citing 3
Cited 1

Markov random field modeling in computer vision

Markov random field modeling in computer vision
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications

Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Unsupervised image segmentation using a simple MRF model with a new implementation scheme

Pattern Recognition

Maximum likelihood blind image separation using nonsymmetrical half-plane Markov random fields

IEEE Transactions on Image Processing

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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.