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Computer-generated holograms (CGHs) are used in holographic three-dimensional(3D) displays and holographic projections. The quality of the reconstructedimages using phase-only CGHs is degraded because the amplitude of thereconstructed image is difficult to control. Iterative optimization methodssuch as the Gerchberg-Saxton (GS) algorithm are one option for improving imagequality. They optimize CGHs in an iterative fashion to obtain a higher imagequality. However, such iterative computation is time consuming, and theimprovement in image quality is often stagnant. Recently, deep learning-basedhologram computation has been proposed. Deep neural networks directly inferCGHs from input image data. However, it is limited to reconstructing imagesthat are the same size as the hologram. In this study, we use deep learning tooptimize phase-only CGHs generated using scaled diffraction computations andthe random phase-free method. By combining the random phase-free method withthe scaled diffraction computation, it is possible to handle a zoomablereconstructed image larger than the hologram. In comparison to the GSalgorithm, the proposed method optimizes both high quality and speed.

Optimization of phase-only holograms calculated with scaled diffraction calculation through deep neural networks