Independent component analysis, a new concept?
Signal Processing - Special issue on higher order statistics
A fast fixed-point algorithm for independent component analysis
Neural Computation
Neural Computation
Source separation in astrophysical maps using independent factor analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image separation using particle filters
Digital Signal Processing
Blind component separation in wavelet space: application to CMB analysis
EURASIP Journal on Applied Signal Processing
A blind source separation technique using second-order statistics
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Deterministic edge-preserving regularization in computed imaging
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A Markov model for blind image separation by a mean-field EM algorithm
IEEE Transactions on Image Processing
On some Bayesian/regularization methods for image restoration
IEEE Transactions on Image Processing
Image Source Separation Using Color Channel Dependencies
ICA '09 Proceedings of the 8th International Conference on Independent Component Analysis and Signal Separation
Fast MCMC separation for MRF modelled astrophysical components
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Adaptive langevin sampler for separation of t-distribution modelled astrophysical maps
IEEE Transactions on Image Processing
Non-stationary t-distribution prior for image source separation from blurred observations
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
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
Blind separation of non-stationary sources using continuous density hidden Markov models
Digital Signal Processing
| Hi-index | 0.03 |
We investigate the source separation problem of random fields within a Bayesian framework. The Bayesian formulation enables the incorporation of prior image models in the estimation of sources. Due to the intractability of the analytical solution, we resort to numerical methods for the joint maximization of the a posteriori distribution of the unknown variables and parameters. We construct the prior densities of pixels using Markov random fields based on a statistical model of the gradient image, and we use a fully Bayesian method with modified-Gibbs sampling. We contrast our work to approximate Bayesian solutions such as Iterated Conditional Modes (ICM) and to non-Bayesian solutions of ICA variety. The performance of the method is tested on synthetic mixtures of texture images and astrophysical images under various noise scenarios. The proposed method is shown to outperform significantly both its approximate Bayesian and non-Bayesian competitors.