Deep learning reconstruction enables full-Stokes single compression in polarized hyperspectral imaging

Abstract

Polarized hyperspectral imaging, which has been widely studied worldwide, can obtain four-dimensional data including polarization, spectral, and spatial domains. To simplify data acquisition, compressive sensing theory is utilized in each domain. The polarization information represented by the four Stokes parameters currently requires at least two compressions. This work achieves full-Stokes single compression by introducing deep learning reconstruction. The four Stokes parameters are modulated by a quarter-wave plate (QWP) and a liquid crystal tunable filter (LCTF) and then compressed into a single light intensity detected by a complementary metal oxide semiconductor (CMOS). Data processing involves model training and polarization reconstruction. The reconstruction model is trained by feeding the known Stokes parameters and their single compressions into a deep learning framework. Unknown Stokes parameters can be reconstructed from a single compression using the trained model. Benefiting from the acquisition simplicity and reconstruction efficiency, this work well facilitates the development and application of polarized hyperspectral imaging.