The research on OPT imaging system combining polarization and super-resolution algorithm

Optical projection tomography (OPT) is a tool used for three-dimensional imaging of millimeter-scale biological samples. For higher image quality, new methods will need to be researched for OPT imaging systems. To make full use of the advantages of light polarization, an OPT image system with a polarization device was built, which can provide polarized projection data. The optimum polarization angle of the polarization device was acquired by experiments. At the optimum polarization angle, the high quality polarized projection data of samples were obtained, the reconstructed tomographic images got more details of samples and the influence of the stray light was eliminated. FSRCNN (Fast Super-Resolution Convolutional Neural Network) based on deep learning was applied for the SR reconstruction of tomographic images. SR tomographic images were assessed by the metrics of image quality and subjective observation. The outline and details of the samples were considerably represented in three-dimensional images reconstructed by SR tomographic images. So, polarization technology and FSRCNN can complement the performance of OPT imaging systems, and enhance imaging ability in the micron range.