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.
Since it was first presented in 2002, the Optical Projection Tomography(OPT) imaging system has emerged as a powerful tool for the study of a biomedical specimen on the mm to cm scale. In this paper, we present a rough and precise algorithm to further improve OPT image acquisition and tomographic reconstruction. The rough and precise algorithm combines the merits of the binarization process and the maximum correlation coefficient, and can accurately correct the displacement of the rotation axis. The tomographic images corrected by the rough and precise algorithm have higher image quality in the simulation experiments and specimen experiments. The reconstructed 3D images based on tomographic images can restore the original specimens. Thereby, the rough and precise algorithm contributes to increasing acquisition speed and quality of OPT data. More work should be performed to better understand and amend the rough and precise algorithm by abundant specimen experiments.
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