压缩感知理论在图像处理领域的应用

朱明; 高文; 郭立强

压缩感知理论在图像处理领域的应用

朱明¹,
高文^1,2,
郭立强¹

1.
中国科学院长春光学精密机械与物理研究所,吉林长春 130033;
2.
中国科学院研究生院,北京 100039

基金项目:

中国科学院二期创新工程基金资助项目(No.C50Top2)

详细信息

中图分类号: TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2011-07-21
- 修回日期: 2011-08-23
- 刊出日期: 2011-10-25

Application of compressed sensing theory in image processing

1.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
2.
Graduate University of Chinese Academy of Sciences, Beijing 100039, China

摘要

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。
- 压缩感知 /
- 采样定理 /
- 成像系统 /
- 图像处理
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.
- compressed sensing /
- sampling theorem /
- imaging system /
- image processing

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参考文献(1)

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