【摘要】 针对传统的采样方法得到的图像数据量巨大,给图像信息的后续处理造成极大压力的问题,对压缩感知理论(Compressed Sensing,CS)进行了研究。压缩感知理论使采集很少一部分数据并且从这些少量数据中重构出更大量信息的想法变成可能,突破了奈奎-斯特采样定理的限制。综述了CS理论及关键技术问题,并着重介绍了CS理论在成像系统、图像融合、图像目标识别与跟踪等方面的应用与发展状况。文章指出CS理论开拓了信息处理的新思路,随着该理论的进一步完善,会有更广泛的应用领域。更多还原

【Abstract】 Traditional Shannon sampling method leads to a large amount of image data,and massive data processing brings a great pressure to bear on the post-processing of image information.Compressed Sensing(CS) theory which can overcome the problem mentioned above is researched in this paper.It can reconstruct a large amount data by sampling small quantity data,and breakthroughs the restriction of Shannon sampling theory.This paper reviews the theory and key technique of CS,and introduces the application and development of CS in imaging system,image fusion,target recognition and tracking.It points out that the CS theory is an effective data processing,and more extensive applications will be come true with the development of the theory.更多还原