Paper No: 65
Blind Separation of Non-stationary Images using Markov Models
Author(s): Rima Guidara, Shahram Hosseini, Yannick Deville
Abstract
In recent works, we presented a blind image separation meth-od 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. |
