针对灰度弱相关的可见光和红外图像的配准问题，本文提出了一种基于改进 SIFT特征的图像配准方法。该方法根据 SIFT算子在仿射变换、加噪、灰度变化等情况下的性能，首先在提取特征点时设定阈值来约束受灰度弱相关影响较大的向量幅值，然后采用性能较稳定的相似四边形的精匹配方式删除粗匹配时的误匹配点对，最后使用最小二乘法求解仿射变换系数的最佳解。实验结果表明，本文提出的基于改进 SIFT特征的匹配方法在匹配精度上有明显改善，复杂度上也可行。

Referring to the problem of the less correlative intensity image registration, an improved approach based on Scale Invariant Features Transform (SIFT) is proposed. First, according to the performance of SIFT in three instances: affine transform, adding Gaussian noise and different intensities, SIFT vector values which was easily affected by different intensities were constrained through given threshold when extracting SIFT feature points. Then, the false matching points were deleted by similar quadrangle in the precise math process. Finally, least squares method was employed to find optimal solutions to affine transform equations. Experimental results show that the proposed approach has a better performance than original SIFT algorithm, and can achieve a high accuracy and moderate complexity level.