Deeplearningassisted Hologram Calculation via LowSampling Holograms Digital holograms can be calculated by simulating light wave propagation on a computer. Hologram calculations are used for threedimensional displays. However, the calculations take a long time, and the data size of the calculated holograms becomes large. This study presents a deeplearningassisted hologram calculation using lowsampling holograms. We calculate holograms with lowsampling rates, resulting in the acceleration of the hologram calculation and the decrease of the hologram size. However, the lowsampling holograms decrease the quality of the reconstructed images and will occur the aliasing errors when not satisfying the Nyquist rate. The proposed method uses a deep neural network to retrieve the fullsampling holograms from the lowsampling holograms. We show elementary results of the proposed method in numerical simulation.

FixedPoint Accuracy Analysis of 2D FFT for the Creation of Computer Generated Holograms Computer Generated Holograms (CGHs) are fundamental to the creation of holographic display and are responsible for carrying the phase or amplitude information of a particular optical field. The standard approaches for CGH generation on personal computers all involve multiple forward and inverse 2dimensional fast Fourier Transforms (2D FFTs). This common method is not fast enough for a realtime application. Producing CGHs via configurable hardware may reduce the computational burden. To reduce overhead, fixedpoint arithmetic is usually implemented; however, this sacrifices the precision of the algorithm. Care needs to be taken when applying fixedpoint operations.In this paper, we present a radix2 error propagation model to analyses the 2D FFT based on three different rounding methods and we also show the simulation results on selection of appropriate rounding methods.