We propose a geometric optimization method combined with the Coulombic energy indicator that can uniformly distribute N polarization states on the Poincaré sphere. Based on this method, we investigate the optimal frames of a rotating polarizer and rotating quarter-wave plate (RPRQ)-based polarization state generator (PSG) at different numbers of modulations. We use the PSG on a dual DoFP polarimeter-based Mueller matrix microscope to measure standard samples and pathological sections for testing the performance of an optimized RPRQ. The experimental results show that this method can effectively restrain noise and improve measurement accuracy.

The Mueller matrix microscope is a powerful tool for characterizing the microstructural features of a complex biological sample. Performance of a Mueller matrix microscope usually relies on two major specifications: measurement accuracy and acquisition time, which may conflict with each other but both contribute to the complexity and expenses of the apparatus. In this paper, we report a learning-based method to improve both specifications of a Mueller matrix microscope using a rotating polarizer and a rotating waveplate polarization state generator. Low noise data from long acquisition time are used as the ground truth. A modified U-Net structured network incorporating channel attention effectively reduces the noise in lower quality Mueller matrix images obtained with much shorter acquisition time. The experimental results show that using high quality Mueller matrix data as ground truth, such a learning-based method can achieve both high measurement accuracy and short acquisition time in polarization imaging.

Polarization imaging can quantitatively probe the microscopic structure of biological tissues which can be complex and consist of layered structures. In this paper, we established a fast-backscattering Mueller matrix imaging system to characterize the dynamic variation in the microstructure of single-layer and double-layer tissues as glycerin solution penetrated into the samples. The characteristic response of Mueller matrix elements, as well as polarization parameters with clearer physics meanings, show that polarization imaging can capture the dynamic variation in the layered microstructure. The experimental results are confirmed by Monte Carlo simulations. Further examination on the accuracy of Mueller matrix measurements also shows that much faster speed has to be considered when backscattering Mueller matrix imaging is applied to living samples.