Separation of nonlinear image mixtures by denoising source separation

Authors:
Mariana S. C. Almeida;Harri Valpola;Jaakko Särelä
Affiliations:
Instituto de Telecomunicações, Lisboa, Portugal;Laboratory of Computational Engineering, Helsinki University of Technology, HUT, Espoo, Finland;Adaptive Informatics Research Centre, Laboratory of Computer and Information Science, Helsinki University of Technology, HUT, Espoo, Finland
Venue:
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Year:
2006

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Abstract

The denoising source separation framework is extended to nonlinear separation of image mixtures. MLP networks are used to model the nonlinear unmixing mapping. Learning is guided by a denoising function which uses prior knowledge about the sparsity of the edges in images. The main benefit of the method is that it is simple and computationally efficient. Separation results on a real-world image mixture proved to be comparable to those achieved with MISEP.