High-Speed Computer-Generated Holography Using Convolutional Neural Networks

Eybposh, M. Hossein; Caira, Nicholas W.; Chakravarthula, Praneeth; Atisa, Mathew; Pégard, Nicolas C.

doi:10.1364/brain.2020.btu2c.2

Cited by 8 publications

(6 citation statements)

References 3 publications

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“…This is relatively easier for DeepCGH with unsupervised training because the ground truth CGH solutions are not explicitly provided. 37,43 The user only needs to provide the specialized dataset to train the model. The adjustable capacity of the CNN model for DeepCGH also introduces a trade-off between hologram fidelity and computation time.…”

Section: Comparison Of Different Cgh Algorithmsmentioning

confidence: 99%

“…CNN-based models can be customized to specific tasks by selecting representative training datasets, and by tailoring the loss function during training. This is relatively easier for DeepCGH with unsupervised training because the ground truth CGH solutions are not explicitly provided 37 , 43 . The user only needs to provide the specialized dataset to train the model.…”

Section: Cgh Algorithmsmentioning

confidence: 99%

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Advances in computer-generated holography for targeted neuronal modulation

et al. 2022

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Genetically encoded calcium indicators and optogenetics have revolutionized neuroscience by enabling the detection and modulation of neural activity with single-cell precision using light. To fully leverage the immense potential of these techniques, advanced optical instruments that can place a light on custom ensembles of neurons with a high level of spatial and temporal precision are required. Modern light sculpting techniques that have the capacity to shape a beam of light are preferred because they can precisely target multiple neurons simultaneously and modulate the activity of large ensembles of individual neurons at rates that match natural neuronal dynamics. The most versatile approach, computer-generated holography (CGH), relies on a computer-controlled light modulator placed in the path of a coherent laser beam to synthesize custom three-dimensional (3D) illumination patterns and illuminate neural ensembles on demand. Here, we review recent progress in the development and implementation of fast and spatiotemporally precise CGH techniques that sculpt light in 3D to optically interrogate neural circuit functions.

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Section: Comparison Of Different Cgh Algorithmsmentioning

confidence: 99%

Section: Cgh Algorithmsmentioning

confidence: 99%

Advances in computer-generated holography for targeted neuronal modulation

et al. 2022

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“…e generated binary holograms by neural network compared with the previous methods and the results were faster than previous work with enhanced quality. In [21], the authors presented computer-generated holography (CGH) which is based on deep learning. e proposed method generates holograms quite fast with high quality, so the performance of the proposed technique is outperform because the baseline methods utilize more computation power and time.…”

Section: Literature Reviewmentioning

confidence: 99%

GAN-Holo: Generative Adversarial Networks-Based Generated Holography Using Deep Learning

Khan

Zhang

et al. 2021

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Current development in a deep neural network (DNN) has given an opportunity to a novel framework for the reconstruction of a holographic image and a phase recovery method with real-time performance. There are many deep learning-based techniques that have been proposed for the holographic image reconstruction, but these deep learning-based methods can still lack in performance, time complexity, accuracy, and real-time performance. Due to iterative calculation, the generation of a CGH requires a long computation time. A novel deep generative adversarial network holography (GAN-Holo) framework is proposed for hologram reconstruction. This novel framework consists of two phases. In phase one, we used the Fresnel-based method to make the dataset. In the second phase, we trained the raw input image and holographic label image data from phase one acquired images. Our method has the capability of the noniterative process of computer-generated holograms (CGHs). The experimental results have demonstrated that the proposed method outperforms the existing methods.

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“…Additionally, the fine-grain control offered by holography can also correct for optical aberrations, provide custom eyeglass prescription correction in software, and enable compact form-factors [Maimone et al 2017], while improving light efficiency compared traditional LCD or OLED displays [Yin et al 2022]. Recent publications have demonstrated significant improvement in hologram image quality [Choi et al 2021a;Maimone et al 2017;Peng et al 2020] and computation time [Eybposh et al 2020;Shi et al 2021], bringing holographic displays one step closer to practicality. However, color holography for AR/VR has remained an open problem.…”

Section: Introductionmentioning

confidence: 99%