creators_name: Almeida, Mariana S. C.
creators_name: Almeida, Luís B.
type: preprint
datestamp: 2006-06-10
lastmod: 2011-03-11 08:56:27
metadata_visibility: show
title: SEPARATING NONLINEAR IMAGE MIXTURES USING A PHYSICAL MODEL TRAINED WITH ICA
subjects: comp-sci-mach-vis
subjects: comp-sci-mach-learn
subjects: comp-sci-neural-nets
subjects: comp-sci-art-intel
full_text_status: public
abstract: This work addresses the separation of real-life nonlinear
mixtures of images, which occur when a paper document is
scanned and the image from the back page shows through.
A physical model of the mixing process, based on the consideration of the halftoning process used to print grayscale images, is presented. The corresponding inverse model is then used to perform image separation. The parameters of the inverse model are optimized through the MISEP technique of nonlinear ICA, which uses an independence criterion based on minimal mutual information.
The quality of the separated images is competitive with
the one achieved by other techniques, namely by MISEP
with a generic MLP-based separation network and by Denoising
Source Separation. The separation results show that
MISEP is an appropriate technique for training the parameters and that the model fits the mixing process well, although not perfectly. Prospects for improvement of the model are presented.
date: 2006
date_type: published
refereed: FALSE
citation:   Almeida, Mariana S. C. and Almeida, Luís B.  (2006) SEPARATING NONLINEAR IMAGE MIXTURES USING A PHYSICAL MODEL TRAINED WITH ICA.  [Preprint]     
document_url: http://cogprints.org/4914/1/modelo.pdf