Speckle-reduced diffractive optical elements beam shaping with regional padding algorithm

Guo, Min; Lv, Guoqiang; Cai, Jiahao; Wang, Zi; Feng, Qibin

doi:10.1117/1.oe.61.12.125103

Opt. Eng.

2022

DOI: 10.1117/1.oe.61.12.125103

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Speckle-reduced diffractive optical elements beam shaping with regional padding algorithm

Min Guo

Guoqiang Lv

Jiahao Cai

et al.

Abstract: Diffractive optical elements (DOEs) have been widely used to realize beam shaping.However, the speckle noise always appears in the reshaped beam, which induces beam quality degradation. We proposed a regional padding algorithm based on the Gerchberg-Saxton algorithm to implement speckle-reduced beam shaping. A variable region is set in the DOE to iterate and optimize the phase of each part DOE. Zero padding is used around the DOE to decrease the sampling interval on the output plane. When the region expands an… Show more

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2024

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Cited by 2 publications

References 38 publications

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Compensated DOE in a VHG-based waveguide display to improve uniformity

Guo,

Cai

et al. 2024

Opt. Express

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Augmented reality head-mounted displays (AR-HMDs) utilizing diffractive waveguides have emerged as a popular research focus. However, the illuminance uniformity over the fields of view (FOV) is often unsatisfactory in volume holographic grating (VHG) based waveguide displays. This paper proposes a high uniformity AR waveguide display system. Firstly, the angular uniformity of the VHG-based waveguide displays is analyzed. Subsequently, diffractive optical elements (DOEs) are seamlessly integrated onto the outer coupling surface of the waveguide substrate to improve the angular uniformity through phase compensation. To design the DOE phase, the multi-objective stochastic gradient descent (MO-SGD) algorithm is proposed. A single DOE is used to compensating various images form the image source. A hybrid loss, which includes the learned perceptual image patch similarity (LPIPS) metric, is applied to enhance the algorithm performance. Simulation results show that the proposed method effectively suppresses illumination degradation at the edge FOV in exit pupil images of the waveguide display system. In the results, the peak signal-to-noise ratio (PSNR) is improved by 5.54 dB. Optical experiments validate the effectiveness of the proposed method. The measured nonuniformity (NU) against FOVs is improved by 53.05% from 0.3749 to 0.1760.

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Compensated DOE in a VHG-based waveguide display to improve uniformity

Guo,

Cai

et al. 2024

Opt. Express

View full text Add to dashboard Cite

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Machine learning based laser homogenization method

Zhang,

Ding,

Hou

et al. 2024

Acta Phys. Sin.

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Laser is widely used in various fields such as laser processing, optical imaging, and optical trapping due to its high monochromaticity, directionality, and high energy density. However, the beam generated by the laser is a Gaussian beam with non-uniform distribution of optical energy, and this non-uniform distribution affects the interaction between the laser and the matter. Therefore, it is necessary to reshape the Gaussian beam into homogenized light spots with uniform distribution of optical energy. Laser beam homogenization method aims to alter the spatial distribution of the Gaussian beam, precisely controlling the shape and intensity of the laser beam to achieve homogenized light spots. However, the existing laser beam homogenization methods suffer from issues such as complicated component preparation and poor flexibility. They also fail to address experimental errors caused by stray light and zero-order light interference, leading to discrepancies between the experimental results and the expected results. These limitations seriously restrict the widespread application of laser technology across various fields. A laser homogenization method based on machine learning is proposed for spatial light modulator (SLM) laser homogenization. The preliminary approach in laser homogenization is to generate a phase hologram using the Gerchberg-Saxton (G-S) algorithm and modulate the incident light beam into homogenized light spots using a SLM. However, the inherent homogenization error of the SLM prevents laser homogenization from improving uniformity. The machine learning method is proposed as a means of compensating for homogenization errors, thereby improving the uniformity of the light spot. The corresponding supervised learning regression task on the experimental dataset establishes mapping relationships between the homogenization target images and the experimental detection images. The results of homogenization error compensation are validated through experiments. Compared to traditional SLM laser homogenization methods, the proposed method reduces the non-uniformity of the light spot by a relative 13%. The laser homogenization method based on machine learning is an efficient way to achieve laser beam homogenization. The proposed laser beam homogenization method can serve as a reference for machine learning-based methods. This method holds significant technical value for laser applications such as laser processing, optical imaging, and optical manipulation. Furthermore, it can provide guidance and a reference point for the utilization of artificial intelligence in addressing optical problems.

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Speckle-reduced diffractive optical elements beam shaping with regional padding algorithm

Cited by 2 publications

References 38 publications

Compensated DOE in a VHG-based waveguide display to improve uniformity

Compensated DOE in a VHG-based waveguide display to improve uniformity

Machine learning based laser homogenization method

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