Learning-based compensation of spatially varying aberrations for holographic display [Invited]

We propose a hologram generation technique to compensate for spatially varying aberrations of holographic displays through machine learning. The image quality of the holographic display is severely degraded when there exist optical aberrations due to misalignment of optical elements or off-axis projection. One of the main advantages of holographic display is that aberrations can be compensated for without additional optical elements. Conventionally, computer-generated holograms for compensation are synthesized through a point-wise integration method, which requires large computational loads. Here, we propose to replace the integration with a combination of fast-Fourier-transform-based convolutions and forward computation of a deep neural network. The point-wise integration method took approximately 95.14 s to generate a hologram of 1024×1024pixels, while the proposed method took about 0.13 s, which corresponds to ×732 computation speed improvement. Furthermore, the aberration compensation by the proposed method is verified through experiments.