传统关联成像系统中对物体全部信息进行采样,而根据压缩感知理论可知绝大部分物体信息在某些变换下具有稀疏特性,因此对物体信息进行稀疏采样也可以复原出完整的物体信息。提出了利用关联成像对物体信息进行稀疏采样的方法,采用成像系统获取物体稀疏信息,再使用压缩感知算法对完整物体信息进行复原。对所提方法进行了实验研究,结果证实了使用稀疏采样能有效减少关联成像的数据量,提高系统的成像效率和质量。

For traditional correlated imaging system, the imaging scene is completely sampled by the illumination speckles. According to the compression sensing theory, most of the object information has sparse characteristics under some transformations, and it can be recovered exactly from a relatively small number of samples. A correlated imaging system with sparse speckles is proposed. Imaging scene is illuminated by sparse speckles, and the information of compressed scene is acquired by compressive imaging system. Finally, the complete scene information is accurately restored using compression sensing algorithm. The experimental results show that sparse speckles can effectively reduce the amount of data to improve the imaging efficiency and the quality of the imaging system.