Time-division multiplexing holographic display using angular-spectrum layer-oriented method (Invited Paper)

2016 ◽  
Vol 14 (1) ◽  
pp. 010005-10009 ◽  
Author(s):  
Yan Zhao Yan Zhao ◽  
Liangcai Cao Liangcai Cao ◽  
Hao Zhang Hao Zhang ◽  
Wei Tan Wei Tan ◽  
Shenghan Wu Shenghan Wu ◽  
...  
Keyword(s):  
Angular Spectrum ◽  
Time Division Multiplexing ◽  
Holographic Display ◽  
Time Division

scholarly journals Enhanced see-through near-eye display using time-division multiplexing of a Maxwellian-view and holographic display

2019 ◽  
Vol 27 (2) ◽  
pp. 689 ◽  
Author(s):  
Jin Su Lee ◽  
Yoo Kwang Kim ◽  
Mu Young Lee ◽  
Yong Hyub Won
Keyword(s):  
Time Division Multiplexing ◽  
Holographic Display ◽  
Maxwellian View ◽  
Time Division

Full-parallax 3D display using time-multiplexing projection technology

2020 ◽  
Vol 2020 (2) ◽  
pp. 100-1-100-6
Author(s):  
Takuya Omura ◽  
Hayato Watanabe ◽  
Naoto Okaichi ◽  
Hisayuki Sasaki ◽  
Masahiro Kawakita
Keyword(s):  
Incident Light ◽  
Polarization State ◽  
3D Image ◽  
3D Display ◽  
Time Division Multiplexing ◽  
3D Images ◽  
Polarization Gratings ◽  
Light Rays ◽  
Time Division

We enhanced the resolution characteristics of a threedimensional (3D) image using time-division multiplexing methods in a full-parallax multi-view 3D display. A time-division light-ray shifting (TDLS) method is proposed that uses two polarization gratings (PGs). As PG changes the diffraction direction of light rays according to the polarization state of the incident light, this method can shift light rays approximately 7 mm in a diagonal direction by switching the polarization state of incident light and adjusting the distance between the PGs. We verified the effect on the characteristics of 3D images based on the extent of the shift. As a result, the resolution of a 3D image with depth is improved by shifting half a pitch of a multi-view image using the TDLS method, and the resolution of the image displayed near the screen is improved by shifting half a pixel of each viewpoint image with a wobbling method. These methods can easily enhance 3D characteristics with a small number of projectors.

A Novel Multichannel Inductive Wear Debris Sensor Based on Time Division Multiplexing

2021 ◽  
Vol 21 (9) ◽  
pp. 11131-11139
Author(s):  
Sen Wu ◽  
Zhijian Liu ◽  
Kezhen Yu ◽  
Zixiao Fan ◽  
Ziyi Yuan ◽  
...  
Keyword(s):  
Wear Debris ◽  
Time Division Multiplexing ◽  
Time Division

scholarly journals Computing 3D Phase-Type Holograms Based on Deep Learning Method

Photonics ◽  
2021 ◽  
Vol 8 (7) ◽  
pp. 280
Author(s):  
Huadong Zheng ◽  
Jianbin Hu ◽  
Chaojun Zhou ◽  
Xiaoxi Wang
Keyword(s):  
Deep Learning ◽  
Signal To Noise Ratio ◽  
Contrast Ratio ◽  
Angular Spectrum ◽  
Structural Similarity ◽  
Computational Time ◽  
Experimental Investigations ◽  
Training Strategy ◽  
Holographic Display ◽  
Phase Type

Computer holography is a technology that use a mathematical model of optical holography to generate digital holograms. It has wide and promising applications in various areas, especially holographic display. However, traditional computational algorithms for generation of phase-type holograms based on iterative optimization have a built-in tradeoff between the calculating speed and accuracy, which severely limits the performance of computational holograms in advanced applications. Recently, several deep learning based computational methods for generating holograms have gained more and more attention. In this paper, a convolutional neural network for generation of multi-plane holograms and its training strategy is proposed using a multi-plane iterative angular spectrum algorithm (ASM). The well-trained network indicates an excellent ability to generate phase-only holograms for multi-plane input images and to reconstruct correct images in the corresponding depth plane. Numerical simulations and optical reconstructions show that the accuracy of this method is almost the same with traditional iterative methods but the computational time decreases dramatically. The result images show a high quality through analysis of the image performance indicators, e.g., peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and contrast ratio. Finally, the effectiveness of the proposed method is verified through experimental investigations.

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