Feasibility analysis and Mueller matrix retrieval strategy for a scene-adaptive modulated imaging polarimeter

Abstract

Multi-domain modulated polarimeters offer benefits such as an increased channel separation in the frequency domain, allowing a reduction in cross-talk and improving their performance in the retrieval of the polarization information. Although the experimental implementation of this kind of system cannot be realized with perfect, periodic modulation due to practical limitations, machine learning methods have been used to obtain the correct calibration parameters and reduce errors during the data reduction stage. The aforementioned strategies have improved the performance of modulated polarimeters. However, in the modulated polarimetric systems reported in the literature, the modulation parameters are set during the design stage and remain unchanged during operation. In this work, we present a dynamic, spatially channeled, imaging Mueller matrix polarimeter in which the modulation parameters of the polarization state generator (PSG) can be adjusted during operation to achieve better performance depending on the spatial frequency properties of the scene under analysis. We present experimental evidence of the feasibility of the method, discuss its capabilities and current limitations, and describe a strategy to retrieve the Mueller matrix of a scene.