为实现双波段红外图像的精确配准，针对云层背景红外光谱辐射特性不一致、采用不同传感器等原因导致大量外点存在的情况，提出了一种引入外点剔除机制的异源图像配准方法。先利用稠密SIFT流对外点进行鲁棒性估计，然后以归一化相关系数作为代价函数，采用基于梯度的方法实现了双波段红外图像的精确配准。实验结果显示，通过剔除外点的方法，能使配准参数快速收敛于全局最优，对相关性较差的双波段红外图像仍能保持较高的配准精度。

Considering the existence of a large number of outliers caused by the difference of infrared spectral radiation properties of cloud background and using different sensors, a new multi-modal registration method with the introduction of outliers rejection mechanism was proposed in order to realize the accurate registration of dual band infrared images. First, outliers was robustly estimated by computing dense SIFT flow. Then, through gradient based framework with the cost function of normalized correlation coefficient the accurate registration of dual band infrared images was achieved. Experimental results show that the registration parameters can converge to the global optimization fleetly after the rejection of outliers, and the algorithm can still maintain high registration accuracy to dual band infrared images with poor correlation.