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. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Almeida, M. S. C., Valpola, H., & Särelä, J. (2006). Separation of nonlinear image mixtures by denoising source separation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3889 LNCS, pp. 8–15). https://doi.org/10.1007/11679363_2
Mendeley helps you to discover research relevant for your work.