基于ROI的高精度红外全景拼接算法研究

针对红外图像空间分辨率低、视场窄,导致图像配准率低、实时性差的问题,提出一种基于感兴趣区域(ROI)的高精度红外全景拼接算法。该算法首先根据两张相邻图像的近似位置关系,求取图像间的ROI；接着,在ROI窗口中提取尺度不变特征变换(SIFT)特征点并将其作为运动目标,结合KLT(Kanade-Lucas-Tomasi)实时跟踪算法确定待配准图像中特征点的位置信息并进行匹配；然后采用随机采样一致性(RANSAC)算法剔除误匹配点对；最后利用像素级融合法消除拼接痕迹,合成一幅分辨率稳定、视场宽的红外全景图像。经实验验证,该算法与传统SIFT算法相比,配准率提高了3.491%,运行时间约提高了50%,能够准确、有效地实现多帧红外图像的无缝拼接。

Research on The method of Infrared Panoramic Image Mosaic Based on ROI

Aiming at the problems of low spatial resolution and narrow field of view of infrared images, which leads to low image registration rate and poor real-time performance, an infrared panoramic mosaic algorithm based on the region of interest (ROI) of image is proposed. The method is built to run as follows:firstly, the ROI of the two adjacent infrared images is calculated according to their position relationship. Then, the SIFT feature points are extracted in the ROI and used as moving targets, thus the feature points in the infrared image to be registered are determined by combining the KLT tracking algorithm. Secondly, the RANSAC algorithm is used to eliminate mismatches. Finally, the pixel level fusion method is used to eliminate the stitching traces and an infrared panoramic image with stable resolution and wide field of view is synthesized. The experimental results show that compared with the traditional SIFT algorithm, the registration rate and the running time of the proposed algorithm is improved by 3.491% and nearly 50%, respectively, which can realize the seamless splicing of multi-frame infrared images accurately and effectively.