• Chinese Optics Letters
  • Vol. 20, Issue 4, 041101 (2022)
Weihao Wang1, Xing Zhao1、2、*, Zhixiang Jiang1, and Ya Wen1
Author Affiliations
  • 1Institute of Modern Optics, Nankai University, Tianjin 300350, China
  • 2Tianjin Key Laboratory of Optoelectronic Sensor and Sensing Network Technology, Tianjin 300350, China
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    DOI: 10.3788/COL202220.041101 Cite this Article Set citation alerts
    Weihao Wang, Xing Zhao, Zhixiang Jiang, Ya Wen. Deep learning-based scattering removal of light field imaging[J]. Chinese Optics Letters, 2022, 20(4): 041101 Copy Citation Text show less

    Abstract

    Light field imaging has shown significance in research fields for its high-temporal-resolution 3D imaging ability. However, in scenes of light field imaging through scattering, such as biological imaging in vivo and imaging in fog, the quality of 3D reconstruction will be severely reduced due to the scattering of the light field information. In this paper, we propose a deep learning-based method of scattering removal of light field imaging. In this method, a neural network, trained by simulation samples that are generated by light field imaging forward models with and without scattering, is utilized to remove the effect of scattering on light fields captured experimentally. With the deblurred light field and the scattering-free forward model, 3D reconstruction with high resolution and high contrast can be realized. We demonstrate the proposed method by using it to realize high-quality 3D reconstruction through a single scattering layer experimentally.
    f=Hg,

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    hij=αj,βiI(x,p)dpdx,

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    U(x,p)=1rexp(ikr),

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    Uoutput(x)=F1{F{Uinput(x)}exp[i4πλd(ωx2+ωy2)]},

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    ϕlens(x)=exp(ik2fx22),

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    ΦMLA(x)=ϕlens(x)*comb(x/dpitch),

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    Woutput(r,u)=Winput(r,u)δ(rrλΔzunr)d2r,

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    Woutput(r,u)=Winput(r,u)δ(uu+rλf)d2u,

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    Woutput(r,u)=Winput(r,u)D(uu)d2u,

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    D(uu)=mAmexp[(uu)2σm2],mZ+,

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    Weihao Wang, Xing Zhao, Zhixiang Jiang, Ya Wen. Deep learning-based scattering removal of light field imaging[J]. Chinese Optics Letters, 2022, 20(4): 041101
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