This paper reviews the development and the applications of the modern compressive imaging in the whole electromagnetic spectra from the perspective of the marginal cost. The values of the compressive imaging need to be considered in the perspective of the hardware cost and the computational expense when the compressive models and the reconstruction algorithms are designed. Based on the plenoptic function, a couple of successful applications are presented to show the advantages in compressing different data. The challenges of the compressive imaging are presented in practical applications. For the large-scale applications of compressive imaging, it is a challenging task to design a compressed sensing model with high stability and good compatibility.

compressed sensing  /  computational imaging  /  plenoptic function