Research on system modeling and data reconstruction for spatial coding compressive spectral imaging

摘要

Compressive spectral imaging is a kind of novel spectral imaging technique that combines traditional spectral imaging method with new concept of compressive sensing. Spatial coding compressive spectral imaging realizes snapshot imaging and the dimension reduction of the acquisition data cube by successive modulation, dispersion and stacking of the light signal. It reduces acquisition data amount, increases imaging signal-to-noise ratio, realizes snapshot imaging for large field of view and has already been applied in the occasions such as high-speed imaging, fluorescent imaging and so on. In this paper, the physical model for single dispersion spatial coding compressive spectral imaging is reviewed on which the data flow procession is analyzed and its reconstruction issue is concluded. The existing sparse reconstruction methods are investigated and specific module based on the two-step iterative shrinkage/thresholding algorithm is built so as to execute the imaging data reconstruction. A regularizer based on the total-variation form is included in the unconstrained minimization problem so that the smooth extent of the restored data cube can be controlled by altering its tuning parameter. To verify the system modeling and data reconstruction method, a simulation imaging experiment is carried out, for which a specific imaging scenery of both spatial and spectral features is firstly built. The root-mean-square error of the whole-band reconstructed spectral images under different regularization tuning parameters are calculated so that the relation between data fidelity and the tuning parameter is revealed. The imaging quality is also evaluated by visual observation and comparison on resulting image and spectral curve.