Improving image quality of a phase-added stereogram using deep learning

Abstract

The disadvantages of computer-generated holograms (CGHs) using the direct integral method are the high computational requirements with increased object points and hologram size. This can be addressed by a phase-added stereogram (PAS), a fast calculation method for CGHs. PAS divides the hologram into small blocks and calculates the point-spread functions (PSFs) of the object points in the Fourier domain of each block. The PSF can be approximated using sparse spectra, which accelerate calculations. However, this approximation degrades the image quality. In this study, we improved the image quality of the PAS using deep learning while maintaining high computational Speed.