基于行扫描测量的运动目标压缩成像

运动目标成像在实际应用中具有重要作用,而如何获取高质量运动目标图像是该领域研究中的一个热点问题.本文采用行扫描采样的方式,通过构造运动测量矩阵,建立一种基于压缩感知理论的运动物体成像模型,并通过仿真及实验,验证了该模型对于恢复运动物体图像信息的可行性.实验结果证明,该方法可获得高质量的运动物体成像.通过引入图像质量评价标准,分析了运动物体成像质量与速度之间的关系.将该方法与普通压缩感知算法进行比较,结果证明,在相同速度下,该方法的成像质量更高.该方法在无人机对地观测、产品线视频监测等领域有着很好的应用前景.

Moving target compressive imaging based on improved row scanning measurement matrix

Moving target imaging(MTI) plays an important role in practical applications. How to capture dynamic images of the targets with high qualities has become a hot point of research in the field of MTI. In order to improve the reconstruction quality, a new MTI model based on compressed sensing(CS) is proposed here, by using a sampling protocol of the row-scanning together with a motion measurement matrix constructed by us. It is proved by the simulation and the experimental results that a relatively high quality can be achieved through this approach. Furthermore, an evaluation criterion of reconstructed image is introduced to analyze the relationship between the imaging quality and the moving speed of the target. By contrast, the performance of our algorithm is much better than that of traditional CS algorithm under the same moving speed condition. As a result, it is suggested that our imaging method may have a great application prospect in the earth observation of unmanned aerial vehicles, video monitoring in the product line and other fields.