Blind separation of sources that have spatiotemporal variance dependencies
Signal Processing - Special issue on independent components analysis and beyond
Estimating Functions for Blind Separation When Sources Have Variance Dependencies
The Journal of Machine Learning Research
An Adaptive Method for Subband Decomposition ICA
Neural Computation
Bayesian separation of images modeled with MRFs using MCMC
IEEE Transactions on Image Processing
Adaptive langevin sampler for separation of t-distribution modelled astrophysical maps
IEEE Transactions on Image Processing
Separating reflections from a single image using spatial smoothness and structure information
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Resolving permutation ambiguity in correlation-based blind image separation
Pattern Recognition Letters
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We investigate the problem of source separation in images in the Bayesian framework using the color channel dependencies. As a case in point we consider the source separation of color images which have dependence between its components. A Markov Random Field (MRF) is used for modeling of the inter and intra-source local correlations. We resort to Gibbs sampling algorithm for obtaining the MAP estimate of the sources since non-Gaussian priors are adopted. We test the performance of the proposed method both on synthetic color texture mixtures and a realistic color scene captured with a spurious reflection.