Maximum likelihood separation of spatially autocorrelated images using a Markov model
Shahram Hosseini, Rima Guidara, Yannick Deville, Christian Jutten · AIP conference proceedings · 2006
We recently proposed a quasi‐efficient maximum likelihood approach for blindly separating Markovian time series. In the present paper, we extend this idea to bi‐dimensional sources (in particular images), where the spatial autocorrelation of each source is described using a second‐order Markov random field. The experimental results using artificial and real images prove the advantage of the method with respect to the maximum likelihood approaches which do not take into account the source autocorrelation, and the autocorrelation‐based methods which ignore the source non‐Gaussianity.