Scheduled System Maintenance:
On May 6th, single article purchases and IEEE account management will be unavailable from 8:00 AM - 12:00 PM ET (12:00 - 16:00 UTC). We apologize for the inconvenience.
By Topic

Separating Nonlinear Image Mixtures using a Physical Model Trained with ICA

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Almeida, M.S.C. ; Inst. de Telecomun., Inst. Super. Tecnico, Lisbon ; Almeida, M.S.C.

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. We present a physical model of the mixing process, based on the consideration of the halftoning process used to print grayscale images. 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 the use of a generic MLP-based separation network instead of the physical model, and by Denoising Source Separation. The separation results show that MISEP is an appropriate technique for estimating the model parameters and that the model fits the mixing process well, although not perfectly. Prospects for improvement of the model are presented.

Published in:

Machine Learning for Signal Processing, 2006. Proceedings of the 2006 16th IEEE Signal Processing Society Workshop on

Date of Conference:

6-8 Sept. 2006