为了提高压缩感知鬼成像的成像质量以及解决低采样率条件下成像失真度高的问题,提出一种基于邻域相似度的鬼成像(NSGI)方案。邻域相似度体现在图像像素间的关联性,携带关于物体结构的重要信息,在分析压缩鬼成像原理的基础上,利用邻域相似度来评价待探测目标。根据贪婪算法的原理,采用邻域相似度优化图像重构过程,并设置相关度阈值降低计算的复杂度。仿真和实验结果均表明,与传统方法相比,该方案可以在低采样率条件下获得高质量低失真度的图像,有利于推动鬼成像技术的实用化。

In order to improve the imaging quality of ghost imaging and solve the problem of high distortion factor under low sampling ratio, we propose a compressive sensing ghost imaging method based on neighbor similarity(NSGI). The neighbor similarity embodied in the correlation between image pixels contains abundant information regarding the spatial structure of the object. We analyze the principle of compressive sensing ghost imaging and use the neighbor similarity to evaluate undetected targets. According to the principle of greedy algorithm, we adopt the neighbor similarity to optimize the process of image reconstruction, and set up the threshold value of the correlation coefficient to reduce computation load and improve precision. The simulation and experimental results show that compared with the traditional ghost imaging, NSGI can obtain high-quality images based on a low sampling frequency, which will further facilitate the practical application of ghost imaging.