Robust Correspondence Detection by L2E Estimator for Retinal Image Registration

Establishing reliable correspondences is often challenging for retinal image registration with poor quality and low overlap. The conventional local search methods usually find many incorrect correspondences by feature descriptor, which would degrade the accuracy of image registration. In this paper, we propose a robust correspondence detection framework for low overlap and poor quality retinal image registration. Specifically, coherent spatial criterion is utilized to remove the false correspondences based on the initial matches in the first hierarchical. And the L2-minimizing estimator is used for further hierarchical discarding significant fraction of outliers and estimating the transformation parameters to align the retinal images by affine model and quadratic model. Since such fitting inliers can be used to preserve the significant correspondences between the fixed image and to-be-aligned image with low overlap, it becomes more efficient to obtain accurate transformation. Through quantitative measurements and visual inspect, our proposed method shows the superior robustness and accuracy to the state-of-the-art methods.