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Maximum contrast analysis for nonnegative blind source separation

journal contribution
posted on 2011-12-01, 00:00 authored by Zuyuan Yang, Yong XiangYong Xiang, S Xie
In this paper, we propose a maximum contrast analysis (MCA) method for nonnegative blind source separation, where both the mixing matrix and the source signals are nonnegative. We first show that the contrast degree of the source signals is greater than that of the mixed signals. Motivated by this observation, we propose an MCA-based cost function. It is further shown that the separation matrix can be obtained by maximizing the proposed cost function. Then we derive an iterative determinant maximization algorithm for estimating the separation matrix. In the case of two sources, a closed-form solution exists and is derived. Unlike most existing blind source separation methods, the proposed MCA method needs neither the independence assumption, nor the sparseness requirement of the sources. The effectiveness of the new method is illustrated by experiments using X-ray images, remote sensing images, infrared spectral images, and real-world fluorescence microscopy images.

History

Journal

Computers and mathematics with applications

Volume

62

Issue

11

Pagination

3997 - 4006

Publisher

Elsevier

Location

Amsterdam, The Netherlands

ISSN

0898-1221

eISSN

1873-7668

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2011, Elsevier