Fourier Optics and Deep Learning Methods for Fast 3D Reconstruction in Digital Holography
This work addresses the need for efficient hologram synthesis in applications like medical imaging or displays, but it appears incremental as it builds on existing optimization and deep learning approaches.
The paper tackles the problem of fast 3D reconstruction in digital holography by proposing a pipeline that uses Fourier optics optimization algorithms and deep learning methods to generate holograms from point cloud and MRI data, resulting in improved performance metrics such as MSE, RMSE, and PSNR through techniques like 2D median filtering.
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.