标题： 傅里叶光学与深度学习方法在数字全息术中快速三维重建的应用 显示英文标题

标题： Fourier Optics and Deep Learning Methods for Fast 3D Reconstruction in Digital Holography

摘要： 计算机生成全息图（CGH）是一种有前景的方法，它使用数字全息图调制用户定义的波形。 提出了一种高效且快速的管道框架，以使用初始点云和MRI数据合成CGH。 此输入数据被重建为体对象，然后输入到非凸傅里叶光学优化算法中，使用交替投影、SGD和拟牛顿方法生成仅相位全息图（POH）和复数全息图（CH）。 还分析了这些算法的重建性能比较，测量指标包括MSE、RMSE和PSNR，以及HoloNet深度学习CGH。 显示在优化过程中使用2D中值滤波去除伪影和斑点噪声可以提高性能指标。 显示英文摘要

摘要： Computer-generated holography (CGH) is a promising method that modulates user-defined waveforms with digital holograms. An efficient and fast pipeline framework is proposed to synthesize CGH using initial point cloud and MRI data. This input data is reconstructed into volumetric objects that are then input into non-convex Fourier optics optimization algorithms for phase-only hologram (POH) and complex-hologram (CH) generation using alternating projection, SGD, and quasi-Netwton methods. Comparison of reconstruction performance of these algorithms as measured by MSE, RMSE, and PSNR is analyzed as well as to HoloNet deep learning CGH. Performance metrics are shown to be improved by using 2D median filtering to remove artifacts and speckled noise during optimization.