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