Super resolution optic three-dimensional imaging based on compressed sensing

A super resolution optic three-dimensional imaging based on compressed sensing was proposed for better optic imaging, in which imaging system was consisted of object glass, coding template, dispersion element, collimating lens, focus lens, detector in the front, hyperspectral data was reconstructed in the end by sparse reconstruction algorithm, so the most of data processing was transformed to the back-end from the imaging system. Meanwhile, Piece reconstruction, dislocation pretreatment and multi-frame reconstruction were used for improving accuracy of reconstruction, reducing memory of the back-processing, lowing computation complexity. By comparing the spectral curve, signal noise ratio, spectral error of the original and the reconstructed data cube, and doing classification and identification analysis, it was gained that the proposed compressed sensing could realize super resolution optic three-dimensional imaging, which have better property in imaging and data application, it can be used in big breath, high resolution, low power consumption and moving-target imaging observation.