Registration of Images with Outliers Using Joint Saliency Map

TL;DR

This paper introduces a joint saliency map (JSM) to improve image registration by addressing outliers and local maxima issues in mutual information-based methods, resulting in more accurate and robust registration of challenging image pairs.

Contribution

The paper presents a novel joint saliency map (JSM) that enhances mutual information-based image registration by effectively handling outliers and local maxima.

Findings

JSM improves registration accuracy in challenging cases.

The method is robust against outliers in image pairs.

Experimental results outperform existing techniques.

Abstract

Mutual information (MI) is a popular similarity measure for image registration, whereby good registration can be achieved by maximizing the compactness of the clusters in the joint histogram. However, MI is sensitive to the "outlier" objects that appear in one image but not the other, and also suffers from local and biased maxima. We propose a novel joint saliency map (JSM) to highlight the corresponding salient structures in the two images, and emphatically group those salient structures into the smoothed compact clusters in the weighted joint histogram. This strategy could solve both the outlier and the local maxima problems. Experimental results show that the JSM-MI based algorithm is not only accurate but also robust for registration of challenging image pairs with outliers.