Holography plays a vital role in the advancement of virtual reality (VR) and augmented reality (AR) display technologies. Its ability to create realistic three‐dimensional (3D) imagery is crucial for providing immersive experiences to users. However, existing computer‐generated holography (CGH) algorithms used in these technologies are either slow or not 3D‐compatible. This article explores four inverse neural network architectures to overcome these issues for real time and 3D applications.
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Zhou et al. (2024) studied this question.
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