结合傅里叶光学与深度学习，实现快速三维全息重建。
Fourier Optics and Deep Learning Methods for Fast 3D Reconstruction in Digital Holography
- 用点云和MRI数据生成体素对象，通过非凸优化生成相位全息图。
- 2D中值滤波显著降低噪声，使重建误差（MSE/ RMSE）和峰值信噪比（PSNR）提升。
- 适合需要高速高精度三维成像的医疗或工业检测场景。
计算全息（CGH）是一种通过数字全息图调制用户定义波形的有前景方法。本文提出一种高效快速的管道框架，利用初始点云和MRI数据合成CGH。输入数据被重构为体素物体，随后输入到非凸傅里叶光学优化算法中，采用交替投影、随机梯度下降（SGD）和拟牛顿法生成仅相位全息图（POH）和复数全息图（CH）。对比了这些算法在均方误差（MSE）、均方根误差（RMSE）和峰值信噪比（PSNR）上的重建性能，并与HoloNet深度学习全息生成方法进行比较。结果显示，在优化过程中使用2D中值滤波可有效去除伪影和散斑噪声，显著提升性能指标。
原文摘要 · Abstract (English)
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.
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