基于Gabor表示特征描述符的红外可见光图像配准

Infrared Visible Image Registration Based on Gabor Representation Feature Descriptor

在无人机航拍场景中,不同传感器捕获的图像可能存在较大视差和分辨率差异,从而导致图像配准失败。针对这一问题,提出一种具有旋转不变Gabor表示特征描述符的红外与可见光图像配准方法。首先,求解图像加权矩阵,将Harris算法应用在相位全等中的加权矩阵方程上实现图像的关键点提取。其次,改进Gabor表示结构,使得关键点在不同方向的特征得到准确定位,从而降低由于视差较大带来的影响。最后,使用最近邻匹配(NNM)算法搭配快速抽样一致性(FSC)实现对异常值的剔除同时增加正确匹配的数量。所提方法在CVC-15立体声、LWIR-RGB长波红外和自制数据集下的平均准确率分别为46%、72%、62%,平均运行时间分别为6.886 s、7.800 s和9.631 s。实验结果验证了该方法在处理具有较大视差和分辨率差异的待配准图像时的有效性。

In the realm of unmanned aerial vehicle aerial photography,images obtained from disparate sensors often exhibit significant parallax and resolution disparities,which can lead to failures in image registration processes.Addressing this challenge,this study introduces an innovative approach for the registration of infrared and visible light images,utilizing a rotationinvariant Gabor representation descriptor.The methodology commences by resolving the image’s weighted matrix,followed by the application of the Harris algorithm to the weighted matrix within the context of phase congruence,thereby pinpointing the image’s key features.Subsequently,the Gabor representation framework is refined to precisely ascertain the orientation of key features,effectively mitigating the impact of substantial parallax.To further enhance the process,the nearest neighbor matching(NNM)algorithm,in tandem with fast sampling consistency(FSC),is deployed to filter out outliers and augment the accuracy of matches.The technique demonstrates an average accuracy of 46%,72%,and 62%across the CVC15 stereo,LWIRRGB longwave infrared,and proprietary datasets,respectively.Correspondingly,the average processing times are 6.886 seconds,7.800 seconds,and 9.631 seconds.Experimental results prove that the efficacy of the proposed method,particularly in scenarios where the images to be registered present considerable parallax and resolution differences.