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9 March 2022 Optimization of time-multiplexed computer-generated holograms with surrogate gradients
M. Hossein Eybposh, Aram Moossavi, Vincent R. Curtis, Nicolas C. Pegard
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Proceedings Volume PC12014, Emerging Digital Micromirror Device Based Systems and Applications XIV; PC1201406 (2022) https://doi.org/10.1117/12.2607781
Event: SPIE OPTO, 2022, San Francisco, California, United States
Abstract
Gradient descent is an efficient algorithm to optimize differentiable functions with continuous variables, yet it is not suitable for computer generated holography (CGH) with binary light modulators. To address this, we replaced binary pixel values with continuous variables that are binarized with a thresholding operation, and we introduced gradients of the sigmoid function as surrogate gradients to ensure the differentiability of the binarization step. We implemented this method both to directly optimize binary holograms, and to train deep learning-based CGH models. Simulations and experimental results show that our method achieves greater speed, and higher accuracy and contrast than existing algorithms.
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M. Hossein Eybposh, Aram Moossavi, Vincent R. Curtis, and Nicolas C. Pegard "Optimization of time-multiplexed computer-generated holograms with surrogate gradients", Proc. SPIE PC12014, Emerging Digital Micromirror Device Based Systems and Applications XIV, PC1201406 (9 March 2022); https://doi.org/10.1117/12.2607781
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KEYWORDS
Computer generated holography
Binary data
Holograms
Image quality
Digital micromirror devices
Modulation
Algorithm development
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