针对常用特征点匹配算法在低对比度图像中存在特征点少、匹配精度低的问题, 将图像自相似性用于图像特征点提取, 并改进特征点匹配过程, 提出了自相似性与改进归一化互相关相结合的方法。该方法首先根据像素点自对称值提取出图像特征点, 然后通过特征点的尺度信息构建自适应相关窗口来改进互相关匹配, 最后由阈值筛选和随机抽样一致性算法优化匹配结果, 从而完成低对比度图像特征点的提取和匹配。实验结果表明, 该方法在匹配低对比度图像特征点时相比常用算法具有较高的效率, 且对图像尺度和旋转变换具有较强的鲁棒性。

As usual methods extract few features in low contrast image and obtain wrong matching result easily, an improved NCC registration based on self-similarity feature was proposed. The method detects feature point by calculating self-similarity value, then constructs the adaptive window according to the scale information of feature point for matching, and improves matching results by threshold filtering and RANSAC algorithm. The experimental results show that the proposed method possesses higher efficiency than usual algorithm in low contrast images, and it has strong robustness to image scale and rotation transformation.