Full-Stokes image reconstruction without polarimetric calibration using a FIRNet

The reconstruction accuracy of polarization images is limited by the performance of the modulators such as polarizers/retarders etc. and mostly requires calibration. Mathematical models are usually used to find the optimal modulation conditions, and involve trade-offs between measurement time and accuracy. In this paper, we propose a full-Stokes image reconstruction (FIR) network to reconstruct polarization images from random modulated images. The network is constructed based on a cycled pixel-to-pixel conditional adversarial network, which has clear advantages in learning the mapping from input images to output images. The network with losses that utilize the physical features between the Stokes vectors is trained, and the reconstruction of complete polarization information is achieved without polarimetric calibration by our method. Simulations and experiments demonstrate the network's wavelength independence and modulation independence, proving the effectiveness and robustness of the FIRNet in this paper.