Research on SIFT polarization image registration method based on matching optimization

摘要

Polarization imaging is another photoelectric imaging detection technology. It has obvious technical advantages in revealing camouflage, penetrating haze, and getting target details. It can gain multiple polarization features images and achieve target detection and recognition through specific polarization information analysis methods such as synthesis and fusion. Because there is a mis-match problem between the polarization features images, polarization image registration performs first. However, existing methods such as mutual information registration and related registration methods are hard to solve the problem of mis-match because of distortion of the polarization imaging lens. In this paper, we present a matching optimization SIFT polarization image registration algorithm found on the standard SIFT registration algorithm. In the sub-matching description, a reversed matching is added, that is, matching in both directions performs to form a symmetrical matching. In the matching set of positive and negative directions, matched feature points pairs satisfying both sets extract. The pair of matching points are only when the pair of feature points are the best matching points. This increases the matching accuracy of feature points and reduces the mismatching rate of descriptions. At the same time, numbers of feature points add in the algorithm using the gray leveling method. Registration experimental results show the registration accuracy of this method is better than the mutual information registration method.