智能系统学报2012，Vol.7Issue(1)：21-32,12.DOI:10.3969/j.issn.1673-4785.201110010
压缩感知理论及其在成像技术中的应用
Compressive sensing theory and its application in imaging technology
摘要
Abstract
Under the guidance of the traditional Shannon/Nyquist sampling theorem, signal processing often faces two problems: technology limitation of the A/D converter and processing pressure caused by a mass of sampled data. Compressive sensing (CS) theory suggests that when the signal is sparse or compressible, by means of global non-adaptive linear projection, all the signal information can be obtained with the samples much less than the sampling theorem required. CS theory based compressive imaging (CI) technology has been developed significantly in recent years. This paper first reviewed the principles of CS, and on this basis, discussed the theory and development of CI technology. The key issues of CI were also analyzed from three aspects of sparse decomposition, construction of measurement matrix, and the reconstruction algorithm. The CI system can significantly cut down on the number of photosensitive devices to avoid resource waste caused by a traditional "sample-then-compress" framework.关键词
压缩感知/压缩成像/稀疏分解/观测矩阵/重建算法Key words
compressive sensing (CS ) / compressive imaging (CI) / sparse decomposition/ measurement matrix/ reconstruction algorithm分类
信息技术与安全科学引用本文复制引用
赵春晖,刘巍..压缩感知理论及其在成像技术中的应用[J].智能系统学报,2012,7(1):21-32,12.基金项目
国家自然科学基金资助项目(61077079) （61077079）
高等学校博士学科点专项基金资助项目(20102304110013) （20102304110013）
哈尔滨市优秀学科带头人基金资助项目(2009RFXXG034). （2009RFXXG034）
