• Matter and Radiation at Extremes
  • Vol. 10, Issue 2, 027402 (2025)
Guo-Guang Li1,2,3,*, Liang Sheng4, Bao-Jun Duan4, Yang Li4..., Yan Song4, Zi-Jian Zhu4, Wei-Peng Yan4, Dong-Wei Hei4 and Qing-Zi Xing1,2,3|Show fewer author(s)
Author Affiliations
  • 1Key Laboratory of Particle and Radiation Imaging (Tsinghua University), Ministry of Education, Beijing 100084, China
  • 2Laboratory for Advanced Radiation Sources and Application, Tsinghua University, Beijing 100084, China
  • 3Department of Engineering Physics, Tsinghua University, Beijing 100084, China
  • 4State Key Laboratory of Intense Pulsed Radiation Simulation and Effect, Northwest Institute of Nuclear Technology, Xi’an 710024, China
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    DOI: 10.1063/5.0236541 Cite this Article
    Guo-Guang Li, Liang Sheng, Bao-Jun Duan, Yang Li, Yan Song, Zi-Jian Zhu, Wei-Peng Yan, Dong-Wei Hei, Qing-Zi Xing. Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play framework[J]. Matter and Radiation at Extremes, 2025, 10(2): 027402 Copy Citation Text show less

    Abstract

    Gamma-ray imaging systems are powerful tools in radiographic diagnosis. However, the recorded images suffer from degradations such as noise, blurring, and downsampling, consequently failing to meet high-precision diagnostic requirements. In this paper, we propose a novel single-image super-resolution algorithm to enhance the spatial resolution of gamma-ray imaging systems. A mathematical model of the gamma-ray imaging system is established based on maximum a posteriori estimation. Within the plug-and-play framework, the half-quadratic splitting method is employed to decouple the data fidelity term and the regularization term. An image denoiser using convolutional neural networks is adopted as an implicit image prior, referred to as a deep denoiser prior, eliminating the need to explicitly design a regularization term. Furthermore, the impact of the image boundary condition on reconstruction results is considered, and a method for estimating image boundaries is introduced. The results show that the proposed algorithm can effectively addresses boundary artifacts. By increasing the pixel number of the reconstructed images, the proposed algorithm is capable of recovering more details. Notably, in both simulation and real experiments, the proposed algorithm is demonstrated to achieve subpixel resolution, surpassing the Nyquist sampling limit determined by the camera pixel size.
    y=(xh)s+ε,

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    y=SHx+n,

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    x=arg minx12ySHx22+λΦ(x),

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    x=arg minx12ySHx22+λΦ(z)s.t.x=z,

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    Lρ(x,z)=12ySHx22+λΦ(z)+ρ2xz22,

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    xk+1=arg minx12ySHx22+ρ2xzk22,

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    zk+1=arg minzλΦ(z)+ρ2zxk+122.

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    zk+1=arg minzΦ(z)+12λ/ρ2zxk+122.

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    zk+1=DRUNetxk+1,λ/ρ.

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    xk+1=ρ1bρ1(SH)TF1F(SHb)|F(h̃0)|2+ρ,

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    x̂k+1=arg minx̂12ySMĤx̂22+ρ2x̂zk22,

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    x̂k+1=arg minx̂12SMĤρIx̂yρzk22,

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    x̂k+1=LSQR(G,r,1e3),

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    Δk=1Nx̂kx̂k122+zkzk122

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    PSNR=10log10MAXI21mni=1mj=1n(xijIij),

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    SSIM=(2μxμI+c1)(2σxI+c2)(μx2+μI2+c1)(σx2+σI2+c2),

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    Contrast=1mi=1mxmax,i1nj=1nxmin,ixmaxxmin×100%,

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    Guo-Guang Li, Liang Sheng, Bao-Jun Duan, Yang Li, Yan Song, Zi-Jian Zhu, Wei-Peng Yan, Dong-Wei Hei, Qing-Zi Xing. Single-image super-resolution of gamma-ray imaging system using deep denoiser prior based on plug-and-play framework[J]. Matter and Radiation at Extremes, 2025, 10(2): 027402
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