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

• URL: http://arxiv.org/abs/2508.06703v1
• Date: Fri, 08 Aug 2025 21:14:36 GMT
• Title: Fourier Optics and Deep Learning Methods for Fast 3D Reconstruction in Digital Holography
• Abstract summary: Computer-generated holography (CGH) is a promising user-defined waveforms with digital holograms.<n>Performance metrics are shown to be improved by using 2D median as well as to HoloNet deep learning CGH.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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