A neural net for blind separation of nonstationary signals
Neural Networks
Second Order Nonstationary Source Separation
Journal of VLSI Signal Processing Systems
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Markovian blind image separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
Maximum likelihood blind image separation using nonsymmetrical half-plane Markov random fields
IEEE Transactions on Image Processing
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In recent works, we presented a blind image separation method based on a maximum likelihood approach, where we supposed the sources to be stationary, spatially autocorrelated and following Markov models. To make this method more adapted to real-world images, we here propose to extend it to non-stationary image separation. Two approaches, respectively based on blocking and kernel smoothing, are then used for the estimation of source score functions required for implementing the maximum likelihood approach, in order to allow them to vary within images. The performance of the proposed algorithm, tested on both artificial and real images, is compared to the stationary Markovian approach, and then to some classical blind source separation methods.