Compressive sensing theory and its application in imaging technology

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.