Table of Contents
ISRN Signal Processing
Volume 2011, Article ID 672353, 9 pages
Research Article

Edge-Detection in Noisy Images Using Independent Component Analysis

1Department of ECE, Concordia University, 1455 de Maisonneuve West, Montreal, QC, Canada H3G 1M8
2EECS Department, University of Toledo, MS 308, 2801 W. Bancroft Street, Toledo, OH 43606, USA

Received 20 January 2011; Accepted 21 February 2011

Academic Editor: F. Palmieri

Copyright © 2011 Kaustubha Mendhurwar et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Edges in a digital image provide important information about the objects contained within the image since they constitute boundaries between objects in the image. This paper proposes a new approach based on independent component analysis (ICA) for edge-detection in noisy images. The proposed approach works in two phases—the training phase and the edge-detection phase. The training phase is carried out only once to determine parameters for the ICA. Once calculated, these ICA parameters can be employed for edge-detection in any number of noisy images. The edge-detection phase deals with transitioning in and out of ICA domain and recovering the original image from a noisy image. Both gray scale as well as colored images corrupted with Gaussian noise are studied using the proposed approach, and remarkably improved results, compared to the existing edge-detection techniques, are achieved. Performance evaluation of the proposed approach using both subjective as well as objective methods is presented.