Neural network for full-Stokes image reconstruction with wavelength and modulation independence

Abstract

In this paper, we propose a full-Stokes image reconstruction (FIR) network to reconstruct polarization images from a snapshot of random modulated image. The Stokes-Mueller matrix theory demonstrates that a image captured by conventional polarization imaging system is a linear superposition of the four Stokes vectors, thus image decomposition methods can be used for full-Stokes image reconstruction. The network is constructed based on a cycled conditional adversarial network, which is trained utilizing the physical mapping relationships between the Stokes vectors and the modulated images. Simulations and experiments are conducted to demonstrate the network’s wavelength independence and modulation independence, proving the effectiveness of proposed FIRnet. To some extent, our method alleviates the contradiction between measurement time and accuracy of polarization, promoting the application of compact snapshot full-Stokes imaging.