Progress of the Computer-Generated Holography Based on Deep Learning
Source abstract
Abstract
With the explosive developments of deep learning, learning–based computer–generated holography (CGH) has become an effective way to achieve real–time and high–quality holographic displays. Plentiful learning–based methods with various deep neural networks (DNNs) have been proposed. In this paper, we focus on the rapid progress of learning–based CGH in recent years. The generation principles and algorithms of CGH are introduced. The DNN structures frequently used in CGH are compared, including U–Net, ResNet, and GAN. We review the developments and discuss the outlook of the learning–based CGH.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Optical Imaging Technologies · Digital Holography and Microscopy · Advanced Vision and Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instruments, Tsinghua University, Beijing 100084, China
Funding
National Natural Science Foundation of China (NSFC) · China Postdoctoral Science Foundation
References (52)
Show source-provided references
1. Frequency-based optimized random phase for computer-generated holographic display10.1364/AO.404934 ↗
2. Holographic capture and projection system of real object based on tunable zoom lens10.1186/s43074-020-0004-3 ↗
3. Time-division multiplexing holographic display using angular-spectrum layer-oriented method (Invited Paper)10.3788/COL201614.010005 ↗
4. Progress in virtual reality and augmented reality based on holographic display10.1364/AO.58.000A74 ↗
5. Three-dimensional deeply generated holography [Invited]10.1364/AO.404151 ↗
6. Domain multiplexed computer-generated holography by embedded wavevector filtering algorithm10.1186/s43074-020-00023-9 ↗
7. Hybrid holographic Maxwellian near-eye display based on spherical wave and plane wave reconstruction for augmented reality display10.1364/OE.418329 ↗
8. Lensless full-color holographic Maxwellian near-eye display with a horizontal eyebox expansion10.1364/OL.437384 ↗
9. Band-limited double-phase method for enhancing image sharpness in complex modulated computer-generated holograms10.1364/OE.414299 ↗
10. High-speed computer-generated holography using an autoencoder-based deep neural network10.1364/OL.425485 ↗
11. Phase-shifting digital holography10.1364/OL.22.001268 ↗
12. Digital holography for quantitative phase-contrast imaging10.1364/OL.24.000291 ↗
13. Assembly of 3-dimensional structures using programmable holographic optical tweezers10.1364/OPEX.12.005475 ↗
14. Fast calculation method for optical diffraction on tilted planes by use of the angular spectrum of plane waves10.1364/JOSAA.20.001755 ↗
15. Fast computation method for a Fresnel hologram using three-dimensional affine transformations in real space10.1364/AO.48.00H212 ↗
16. Ogihara, Y., Ichikawa, T., and Sakamoto, Y. (2014, January 1–6). Fast calculation with point-based method to make CGHs of the polygon model. Proceedings of the SPIE—The International Society for Optical Engineering, San Francisco, CA, USA.10.1117/12.2039821 ↗
17. Polygon-based computer-generated holography: A review of fundamentals and recent progress [Invited]10.1364/AO.444973 ↗
18. Accurate calculation of computer-generated holograms using angular-spectrum layer-oriented method10.1364/OE.23.025440 ↗
19. Improved look-up table method of computer-generated holograms10.1364/AO.55.009255 ↗
20. Foveated computer-generated hologram and its progressive update using triangular mesh scene model for near-eye displays10.1364/OE.27.023725 ↗
21. Fast calculation method with foveated rendering for computer-generated holograms using an angle-changeable ray-tracing method10.1364/AO.58.00A258 ↗
22. Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5–9). U-Net: Convolutional Networks for Biomedical Image Segmentation. Proceedings of the Medical Image Computing and Computer-Assisted Intervention—MICCAI 2015, Munich, Germany.10.1007/978-3-319-24574-4_28 ↗
23. Deep-learning-generated holography10.1364/AO.57.003859 ↗
24. Deep-learning-based binary hologram10.1364/AO.393500 ↗
25. Deep-learning-based hologram generation using a generative model10.1364/AO.427262 ↗
26. Learning-based compensation of spatially varying aberrations for holographic display [Invited]10.1364/JOSAA.444613 ↗
27. GAN-Holo: Generative Adversarial Networks-Based Generated Holography Using Deep Learning10.1155/2021/6662161 ↗
28. High-speed computational ghost imaging based on an auto-encoder network under low sampling rate10.1364/AO.422641 ↗
29. Deep neural network for multi-depth hologram generation and its training strategy10.1364/OE.402317 ↗
30. The state-of-the-art in computer generated holography for 3D display
31. Deep-Learning Computational Holography: A Review (Invited)
32. Fast calculation of computer-generated-hologram on AMD HD5000 series GPU and OpenCL10.1364/OE.18.009955 ↗
33. Hardware implementations of computer-generated holography: A review
34. Computer-generated holography as a generic display technology10.1109/MC.2005.260 ↗
35. Recent progress in computer-generated holography for three-dimensional scenes10.1080/15980316.2016.1255672 ↗
36. Stereoscopic approach to 3-D display using computer-generated holograms10.1364/AO.15.002722 ↗
37. Modulation transfer function and optimum sampling of holographic stereograms10.1364/AO.33.000768 ↗
38. Holographic stereogram from sequential component photographs10.1063/1.1651831 ↗
39. A logical calculus of the ideas immanent in nervous activity10.1016/S0092-8240(05)80006-0 ↗
40. ImageNet classification with deep convolutional neural networks10.1145/3065386 ↗
41. Fully Convolutional Networks for Semantic Segmentation10.1109/TPAMI.2016.2572683 ↗
42. Milletari, F., Navab, N., and Ahmadi, S. (2016, January 25–28). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.10.1109/3DV.2016.79 ↗
43. Jégou, S., Drozdzal, M., Vázquez, D., Romero, A., and Bengio, Y. (2017, January 21–26). The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Honolulu, HI, USA.10.1109/CVPRW.2017.156 ↗
44. He, K., and Sun, J. (2015, January 7–12). Convolutional neural networks at constrained time cost. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.10.1109/CVPR.2015.7299173 ↗
45. Srivastava, R.K., Greff, K., and Schmidhuber, J. (2015). Highway Networks. arXiv.
46. He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27–30). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.10.1109/CVPR.2016.90 ↗
47. Generative adversarial networks10.1145/3422622 ↗
48. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016, January 5–10). Improved Techniques for Training GANs. Proceedings of the 30th International Conference on Neural Information Processing Systems, Barcelona, Spain.
49. DeepCGH: 3D computer-generated holography using deep learning10.1364/OE.399624 ↗
50. Towards real-time photorealistic 3D holography with deep neural networks10.1038/s41586-020-03152-0 ↗
51. Cohen, G., Afshar, S., Tapson, J., and van Schaik, A. (2017, January 14–19). EMNIST: Extending MNIST to handwritten letters. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.10.1109/IJCNN.2017.7966217 ↗
52. Generative adversarial network in medical imaging: A review10.1016/j.media.2019.101552 ↗