Binary-Phase Computer-Generated Holography using Hardware-in-the-loop Feedback

Kadis, Andrew; Mouthaan, Ralf; Dong, Daoming; Wang, Youchao; Wetherfield, Benjamin; Guendy, Miguel El; Wilkinson, Timothy D.

doi:10.1364/dh.2021.dw5e.1

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…The following research is an extension of the camera-in-theloop holography: high-quality holographic display using partially coherent light (LED light source) (Peng et al, 2021), holographic display using Michelson setup to eliminate undiffracted light of SLM (Choi et al, 2021), optimizing binary phase holograms (Kadis et al, 2021), holographic display that suppresses highorder diffracted light using only computational processing without any physical filters (Gopakumar et al, 2021), and further improvement of image quality by using a Gaussian filter to remove noise that is difficult to optimize (Chen et al, 2022).…”

Section: Camera-in-the-loop Holographymentioning

confidence: 99%

Deep-Learning Computational Holography: A Review

et al. 2022

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Deep learning has been developing rapidly, and many holographic applications have been investigated using deep learning. They have shown that deep learning can outperform previous physically-based calculations using lightwave simulation and signal processing. This review focuses on computational holography, including computer-generated holograms, holographic displays, and digital holography, using deep learning. We also discuss our personal views on the promise, limitations and future potential of deep learning in computational holography.

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Section: Camera-in-the-loop Holographymentioning

confidence: 99%

Deep-Learning Computational Holography: A Review

et al. 2022

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“…The key research challenge in CGH is how to compute the set of holographic fringes which correspond to a desired wavefront. There exists a large number of established algorithmic approaches to solve this problem, such as direct-search [18], phase-retrieval algorithms [19,20], simulated-annealing [21], noise reduction time-multiplexing [22], double-phase methods [23], hardware feedback [24,25], frequency-domain approaches [26] and formal optimisation [27]. The exact choice depends on the specific optical requirements, display device type selected and the specific computational hardware available [28,29,30].…”

Section: Introductionmentioning

confidence: 99%

Effect of Bit-depth in Stochastic Gradient Descent Performance for Phase-only Computer-generated Holography Displays

Kadis¹,

Wetherfield²,

Sha³

et al. 2022

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SGD (Stochastic gradient descent) is an emerging technique for achieving high-fidelity projected images in CGH (computergenerated holography) display systems. For real-world applications, the devices to display the corresponding holographic fringes have limited bit-depth depending on the specific display technology employed. SGD performance is adversely affected by this limitation and in this piece of work we quantitatively compare the impact on algorithmic performance based on different bit-depths by developing our own algorithm, Q-SGD (Quantised-SGD). The choice of modulation device is a key decision in the design of a given holographic display systems and the research goal here is to better inform the selection and application of individual display technologies.

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