Four-dimensional compressed spectropolarimetric imaging

Abstract

Polarized hyperspectral images contain rich information representing both surface texture and spectral signature of target scene. However, it is difficult to obtain the full-Stokes parameters of hyperspectral images directly. A compressive imaging system is proposed in this paper to recover the four-dimensional polarized hyperspectral images with full-Stokes parameters using quarter-wave plate (QWP) and liquid crystal tunable filter (LCTF). Changing the fast axis angle of QWP provides the degrees of freedom to modulate the polarization states. The LCTF serves as the broad-band spectral filter to modulate the spectral signatures. The output of LCTF is modulated by a coded aperture in spatial domain, and then the modulated polarized hyperspectral images are projected and multiplexed on a two-dimensional detector. Based on the compressive sensing theory, the full-Stokes polarized hyperspectral images can be reconstructed from several compressive measurements by solving the convex optimization problems with sparsity prior. The feasibility of proposed system is verified by simulations and experiments. Compared to the traditional compressive spectropolarimetric imaging methods, the proposed method is beneficial to reduce the compression rate, and thus shorten the data acquisition time. This work also paves a new way for the full-Stokes polarized hyperspectral imaging system with simple and compact structure.