Multi-modal image matching based on local frequency information

This paper challenges the issue of matching between multi-modal images with similar physical structures but different appearances. To emphasize the common structural information while suppressing the illumination and sensor-dependent information between multi-modal images, two image representations namely Mean Local Phase Angle (MLPA) and Frequency Spread Phase Congruency (FSPC) are proposed by using local frequency information in Log-Gabor wavelet transformation space. A confidence-aided similarity (CAS) that consists of a confidence component and a similarity component is designed to establish the correspondence between multi-modal images. The two representations are both invariant to contrast reversal and non-homogeneous illumination variation, and without any derivative or thresholding operation. The CAS that integrates MLPA with FSPC tightly instead of treating them separately can more weight the common structures emphasized by FSPC, and therefore further eliminate the influence of different sensor properties. We demonstrate the accuracy and robustness of our method by comparing it with those popular methods of multi-modal image matching. Experimental results show that our method improves the traditional multi-modal image matching, and can work robustly even in quite challenging situations (e.g. SAR & optical image).