On the relationship of the Markov mesh to the NSHP Markov chain
Pattern Recognition Letters
A neural net for blind separation of nonstationary signals
Neural Networks
Second Order Nonstationary Source Separation
Journal of VLSI Signal Processing Systems
Equivariant nonstationary source separation
Neural Networks
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Adaptive Blind Signal and Image Processing: Learning Algorithms and Applications
Blind component separation in wavelet space: application to CMB analysis
EURASIP Journal on Applied Signal Processing
Blind spectral unmixing by local maximization of non-Gaussianity
Signal Processing
Blind separation of non-stationary images using Markov models
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
An MRF-ICA based algorithm for image separation
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
Markovian blind image separation
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Blind separation of mixture of independent sources through aquasi-maximum likelihood approach
IEEE Transactions on Signal Processing
Equivariant adaptive source separation
IEEE Transactions on Signal Processing
Blind separation of instantaneous mixtures of nonstationary sources
IEEE Transactions on Signal Processing
A Markov model for blind image separation by a mean-field EM algorithm
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Blind source separation by nonstationarity of variance: a cumulant-based approach
IEEE Transactions on Neural Networks
A fast mixing matrix estimation method in the wavelet domain
Signal Processing
Smoke Detection in Video: An Image Separation Approach
International Journal of Computer Vision
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This paper presents a maximum likelihood approach for blindly separating linear instantaneous mixtures of images. The spatial autocorrelation within each image is described using non-symmetrical half-plane (NSHP) Markov random fields in order to simplify the joint probability density functions of the source images. A first implementation assuming stationary sources is presented. It is then extended to a more realistic nonstationary image model: two approaches, respectively based on blocking and kernel smoothing, are proposed to cope with the nonstationarity of the images. The estimation of the mixing matrix is performed using an iterative equivariant version of the Newton-Raphson algorithm. Moreover, score functions, required for the computation of the updating rule, are approximated at each iteration by parametric polynomial estimators. Results achieved with artificial mixtures of both artificial and real-world images, including an astrophysical application, clearly prove the high performance of our methods, as compared to classical algorithms.