Deep learning for denoising in a Mueller matrix microscope

Abstract

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.