Multiplane Optimizing Phase Holograms Using Advanced Machine Learning Algorithms and GPU Acceleration

Phase holography is a critical optical imaging and information processing technique with applications ranging from microscopy to optical communications. However, optimizing phase hologram generation remains a significant challenge due to the non-convex nature of the optimization problem. This paper presents a novel multiplane optimization approach for phase hologram generation to minimize the reconstruction error across multiple focal planes. We significantly improve holographic reconstruction quality by integrating advanced machine learning algorithms like RMSprop and Adam with GPU acceleration. The proposed method utilizes TensorFlow to implement custom propagation layers, optimizing the phase hologram to reduce errors at strategically selected distances.