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• Enables four-dimensional imaging in the polarization, spectral and spatial domains. • LCTF plays both a high-resolution hyperspectral filter and a joint linear polarizer. • QWP joins LP to measure full-Stokes parameters of hyperspectral images compressively. • It uses precise solving instead of fuzzy reconstructing the first Stokes parameter. • Improving measurement approach can further separate the last three Stokes parameters. Polarization hyperspectral compressive imaging has wide application prospects due to reflecting rich information of the target from the spatial, spectral and polarization dimensions. However, compressive imaging methods are limited in system complexity and reconstruction efficiency. In this paper, the combination of a quarter-wave plate, a linear polarizer and a liquid crystal tunable filter enables compressive full-Stokes polarization and flexible hyperspectral imaging. Then, the four Stokes parameters can be quickly reconstructed with high precision through numerical operations and compressive sensing. Both simulations and experiments verify the effectiveness of this method. Compared with the feature scaling method, this work shortens the reconstruction time by 83.73%, and greatly improves the reconstruction result of the first Stokes parameter from limited reconstruction to accurate solution. This work not only gives insights from efficient reconstruction to system design, but also creates conditions for the application of polarization hyperspectral imaging in specific fields. [ABSTRACT FROM AUTHOR]

• Enables simultaneous compression in the polarization, spectral and spatial domains. • Change the fast axis angle of QWP 2 or 3 times to reconstruct full-Stokes parameters. • LCTF with dense modulation function encodes and reconstructs high-resolution spectra. • Combine DMD encoding and detector multiplexing to achieve spatial super-resolution. • It's beneficial to reduce the compression rate, so shorten the data acquisition time. 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. [ABSTRACT FROM AUTHOR]

Hyperspectral polarized images are widely used in target detection, and compressive sensing has been developed to shorten the acquisition time and reduce the memory usage. However, the current systems have defects in measuring the polarization information, and current sparse bases meet problems in reconstructing process. This paper combines compressive sensing and machine learning to obtain polarized images. Both the simpler system and the sparse basis optimizing method are developed. Liquid crystal tunable filter (LCTF) serves as both hyperspectral filter and linear polarizer. The LCTF compresses the first three Stokes parameters by changing the transmission axis angle of its incident surface. The LCTF and quarter-wave plate (QWP) compress all four Stokes parameters by changing the fast axis angle of QWP. Then, the Stokes parameters can be reconstructed based on the sparse basis optimized by particle swarm optimization (PSO) algorithm. The sparse basis optimized through the spatially local images of a target in one spectral band also works for both global images of this target in many other spectral bands and images of other targets. • LCTF or LCTF combined with QWP can compress linear or all four Stokes parameters. • Introduce chemometric methods to optimize the sparse basis of Stokes parameters. • Basis optimized by PSO works for targets and bands that are not involved in modeling. • Compared to common bases, optimized sparse basis improves reconstruction accuracy. [ABSTRACT FROM AUTHOR]