E-mail Alert
RSS
| Citation: |
Bianco V, Ferraro P. Advancing computer-generated holographic display thanks to diffraction model-driven deep nets. Opto-Electron Adv 7, 230176 (2024). doi: 10.29026/oea.2024.230176
|
News & Views Open Access
Advancing computer-generated holographic display thanks to diffraction model-driven deep nets
-
Abstract
Advancements are reported in computer-generated holography proofing RGB 4K display through a new strategy based on diffraction model-driven deep networks. In the new 4K-DMDNet, the network is not a “black box” anymore. Rather, the input-output relation must obey to the physics of wavefront propagation, which is embedded here as a constraint. Thus, a labelled dataset is not required, and the model shows superior generalization capabilities with respect to data-driven approaches. The method is promising for the new generation of RGB 4K holographic display, as well as augmented and virtual reality systems. -
-
References
[1] Zhang CL, Zhang DF, Bian ZP. Dynamic full-color digital holographic 3D display on single DMD. Opto-Electron Adv 4, 200049 (2021). doi: 10.29026/oea.2021.200049 [2] Shi L, Li BC, Kim C et al. Towards real-time photorealistic 3D holography with deep neural networks. Nature 591, 234–239 (2021). doi: 10.1038/s41586-020-03152-0 [3] He ZH, Sui XM, Jin GF et al. Progress in virtual reality and augmented reality based on holographic display. Appl Opt 58, A74–A81 (2019). doi: 10.1364/AO.58.000A74 [4] Bianco V, D'Agostino M, Pirone D et al. Label‐free intracellular multi‐specificity in yeast cells by phase‐contrast tomographic flow cytometry. Small Methods 7 (2023). doi: 10.1002/smtd.202300447 [5] Sahin E, Stoykova E, Mäkinen J et al. Computer-generated holograms for 3D imaging: a survey. ACM Comput Surv 53, 32 (2021). doi: 10.1145/3378444 [6] Gerchberg RW. A practical algorithm for the determination of phase from image and diffraction plane pictures. Optik 35, 237–246 (1972). [7] Chakravarthula P, Peng YF, Kollin J et al. Wirtinger holography for near-eye displays. ACM Trans Graph 38, 213 (2019). doi: 10.1145/3355089.3356539 [8] Zhang JZ, Pégard N, Zhong JS et al. 3D computer-generated holography by non-convex optimization. Optica 4, 1306–1313 (2017). doi: 10.1364/OPTICA.4.001306 [9] Liu KX, Wu JC, He ZH et al. 4K-DMDNet: diffraction model-driven network for 4K computer-generated holography. Opto-Electron Adv 6, 220135 (2023). doi: 10.29026/oea.2023.220135 [10] Peng YF, Choi S, Kim J et al. Speckle-free holography with partially coherent light sources and camera-in-the-loop calibration. Sci Adv 7, eabg5040 (2021). doi: 10.1126/sciadv.abg5040 [11] Ishii Y, Shimobaba T, Blinder D et al. Optimization of phase-only holograms calculated with scaled diffraction calculation through deep neural networks. Appl Phys B 128, 22 (2022). doi: 10.1007/s00340-022-07753-7 [12] Yu T, Zhang SJ, Chen W et al. Phase dual-resolution networks for a computer-generated hologram. Opt Express 30, 2378–2389 (2022). doi: 10.1364/OE.448996 [13] Wu JC, Liu KX, Sui XM et al. High-speed computer-generated holography using an autoencoder-based deep neural network. Opt Lett 46, 2908–2911 (2021). doi: 10.1364/OL.425485 [14] Ferraro P, De Nicola S, Coppola G et al. Controlling image size as a function of distance and wavelength in Fresnel-transform reconstruction of digital holograms. Opt Lett 29, 854–856 (2004). doi: 10.1364/OL.29.000854 -
Access History
Figures(1)
Article Metrics
Export File
Citation
Bianco V, Ferraro P. Advancing computer-generated holographic display thanks to diffraction model-driven deep nets. Opto-Electron Adv 7, 230176 (2024). doi: 10.29026/oea.2024.230176
Format
Content
DownLoad:
-
Figure 1.
Conceptual scheme of the generation and reconstruction process of 4K POHs by the 4KDMDNet.
