一种鲁棒的红外与可见光多级景象匹配算法

针对基于特征的景象匹配方法对图像噪声和局部形变适应性差以及对特征质量依赖性强等问题,从提高图像特征描述能力入手,将具有局部光照和对比度不变性的相位一致性变换引入到红外与可见光景象匹配的特征表征中,以获得对噪声和局部形变较强的适应性。在此基础上,首先采用抗旋转的圆投影实现图像粗略匹配,然后基于Zernike矩推导出图像的互相关匹配重构函数并用来剔除错误匹配点,最后基于曲面拟合方法获得亚像素定位,从而实现图像精确与鲁棒匹配。仿真实验验证了所提算法的有效性。

A Robust Multi-level Scene Matching Algorithm for Infrared and Visible Light Image

To tackle the problem that image matching methods based on feature have great dependence on feature’s quality and have poor adaptability to image noise and local distortion for multi-modal images, this article starts from enhancing the representation ability of images. Then, phase congruency transformation with local intensity and contrast invariance is used to represent the infrared images and visible light images to increase the robustness of scene matching. Based on this, the ring projection transformation is first adopted to get these coarse matching positions. Then, the cross-correlation reconstruction function is inferred using Zernike moment to remove these wrong matching positions. Finally, curve fitting method is used to get sub-pixel localization and realize image accuracy and robust matching. Experimental results demonstrate the effectiveness of this proposed algorithm.