Edge-Detection in Noisy Images Using Independent Component Analysis

Abstract EN

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.