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Compressive sensing for channeled polarimetry: applications in spectropolarimetry and imaging polarimetry

This paper will present an overview of compressive sensing for channeled polarimetry. We frame the reconstruction of the Stokes parameters as an underdetermined problem, where we solve for 3N unknowns from N measurements. We discuss two types of polarimeters: channeled spectropolarimeters and channeled linear imaging polarimeters. The polarimeters may differ in a few aspects: the output may be signals or images, the optical elements may vary, and the dimensions may be spatial or spectral. Our algorithms work with existing polarimeters and require no change in optical elements or measurement procedure. The purpose of this work is to present this framework and describe how it applies across different types of polarimeters. Both simulations and experiments show that our algorithms produce more accurate reconstructions with less artifacts than frequency domain filtering