Digital holographic 3D surface topography measurement based on recording-plane rotation

Song, Qing; Xi, Jiamin; Wang, Shirong; Xia, Haiting; Cai, Yangweinan; Guo, Rongxin

doi:10.1016/j.optcom.2023.129801

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…Holographic technology has the capability to reproduce the seven-dimensional light field function completely, which is regarded as the most perfect display technology. [1] It has diverse applications in measurement, [2][3][4][5][6][7] display, [8][9][10][11][12] storage, [13][14][15] etc. Computer-generated hologram (CGH) offers lower noise and is unaffected to optical path.…”

Section: Introductionmentioning

confidence: 99%

Accelerated generation of holograms with ultra-low memory symmetrically high-compressed look-up table

Yang 杨,

Zhu 朱,

Sun 孙

et al. 2024

Chinese Phys. B

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Computer-generated holography technology has been widely applied, and as research in this field deepens, the demand for memory and computational power in small AR and VR devices continues to increase. This paper introduces a hologram generation method, symmetrically high-compressed look-up table method, which can reduce memory usage by 50%. In offline computing, the half of basic horizontal and vertical modulation factors are stored, halving the memory requirements without affecting inline speed. Currently, its potential extends to various holographic applications, including the production of optical diffraction elements.

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Section: Introductionmentioning

confidence: 99%

Accelerated generation of holograms with ultra-low memory symmetrically high-compressed look-up table

Yang 杨,

Zhu 朱,

Sun 孙

et al. 2024

Chinese Phys. B

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Illumination system contributing zooming function to lensless digital holographic microscope by using lightguide incorporated with volume holographic optical elements

Yu,

Wang,

Chen

et al. 2024

Optics & Laser Technology

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Unsupervised speckle denoising in digital holographic interferometry based on 4-f optical simulation integrated cycle-consistent generative adversarial network

Yu,

Fang,

Song

et al. 2024

Appl. Opt.

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The speckle noise generated during digital holographic interferometry (DHI) is unavoidable and difficult to eliminate, thus reducing its accuracy. We propose a self-supervised deep-learning speckle denoising method using a cycle-consistent generative adversarial network to mitigate the effect of speckle noise. The proposed method integrates a 4-f optical speckle noise simulation module with a parameter generator. In addition, it uses an unpaired dataset for training to overcome the difficulty in obtaining noise-free images and paired data from experiments. The proposed method was tested on both simulated and experimental data, with results showing a 6.9% performance improvement compared with a conventional method and a 2.6% performance improvement compared with unsupervised deep learning in terms of the peak signal-to-noise ratio. Thus, the proposed method exhibits superior denoising performance and potential for DHI, being particularly suitable for processing large datasets.

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Digital holographic 3D surface topography measurement based on recording-plane rotation

Cited by 3 publications

References 28 publications

Accelerated generation of holograms with ultra-low memory symmetrically high-compressed look-up table

Accelerated generation of holograms with ultra-low memory symmetrically high-compressed look-up table

Illumination system contributing zooming function to lensless digital holographic microscope by using lightguide incorporated with volume holographic optical elements

Unsupervised speckle denoising in digital holographic interferometry based on 4-f optical simulation integrated cycle-consistent generative adversarial network

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