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基于稀疏约束的傅里叶变换鬼成像空间复用重建

Spatial multiplexing reconstruction for Fourier-transform ghost imaging via sparsity constraints.

作者信息

Zhu Ruiguo, Yu Hong, Lu Ronghua, Tan Zhijie, Han Shensheng

出版信息

Opt Express. 2018 Feb 5;26(3):2181-2190. doi: 10.1364/OE.26.002181.

DOI:10.1364/OE.26.002181
PMID:29401758
Abstract

A spatial multiplexing reconstruction method has been proposed to improve the sampling efficiency and image quality of Fourier-transform ghost imaging. In this method, the sensing equation of Fourier-transform ghost imaging is established based on recombination and reutilization of the correlated intensity distributions of light fields. It is theoretically proved that the scale of the sensing matrix in the sensing equation can be greatly reduced, and spatial multiplexing combined with this matrix reduction provides the feasibility of ghost imaging with just a few measurements. Experimental results show better visibility and signal-to-noise ratio in the Fourier spectrums reconstructed via spatial multiplexing compared with previous methods. The transmittance of an object is also recovered in spatial domain with better image quality based on its spectrum of spatial multiplexing reconstruction. This method is especially important to x-ray ghost imaging applications due to its potential for reducing radiation damage and achieving high quality images in x-ray microscopy.

摘要

为了提高傅里叶变换鬼成像的采样效率和图像质量,提出了一种空间复用重建方法。在该方法中,基于光场相关强度分布的重组和再利用,建立了傅里叶变换鬼成像的传感方程。理论证明,传感方程中传感矩阵的规模可以大大减小,空间复用与这种矩阵缩减相结合,为只需少量测量的鬼成像提供了可行性。实验结果表明,与以前的方法相比,通过空间复用重建的傅里叶频谱具有更好的可见性和信噪比。基于其空间复用重建的频谱,还可以在空间域中恢复物体的透过率,且图像质量更好。由于该方法在减少辐射损伤和在x射线显微镜中获得高质量图像方面具有潜力,因此对x射线鬼成像应用尤为重要。

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引用本文的文献

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Multiperspective Light Field Reconstruction Method via Transfer Reinforcement Learning.基于迁移强化学习的多角度光场重建方法。
Comput Intell Neurosci. 2020 Feb 14;2020:8989752. doi: 10.1155/2020/8989752. eCollection 2020.