Chinese Journal of Lasers, Volume. 51, Issue 15, 1507103(2024)

Flow‐Based Model for Fluorescence Image Super‐Resolution

Junchao Fan1, MiaoYunyun1, XiuLi Bi1, Bin Xiao1、*, and Xiaoshuai Huang2
Author Affiliations
  • 1Chongqing Key Laboratory of Image Cognition, College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
  • 2Biomedical Engineering Department, Peking University, Beijing 100191, China
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    Figures & Tables(15)
    Multi-frame dual-domain attention flow network
    Microscope captures live cell samples to obtain wide-field images of 20 consecutive time points
    Frequency-spatial joint attention module
    Results of F-actin data set under different σ. (a) Objective results of reconstructed super-resolution images; (b) subjective results of reconstructed super-resolution images
    Results of ER data set under different σ. (a) Objective results of reconstructed super-resolution images; (b) subjective results of reconstructed super-resolution images
    Results of CCPs data set under different σ. (a) Objective results of reconstructed super-resolution images; (b) subjective results of reconstructed super-resolution images
    Comparison of single- and multi-frame experimental results on F-actin data set
    Comparison of single- and multi-frame experimenal results on ER data set
    Comparison of single- and multi-frame experimental results on CCPs data set
    Comparison of reconstruction results using different methods on F-actin data set
    Comparison of reconstruction results using different methods on ER data set
    Comparison of reconstruction results using different methods on CCPs data set
    Resolution comparison of reconstruction results using different methods on different data sets. (a) F-actin data set; (b) ER data set; (c) CCPs data set
    • Table 1. Analysis of super resolution results of single- and multi-frame experiments

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      Table 1. Analysis of super resolution results of single- and multi-frame experiments

      ModelF-actinERCCPs
      PSNRLPIPSPSNRLPIPSPSNRLPIPS
      SDAFN26.570.275125.770.188827.730.2637
      MDAFN27.240.238226.030.185528.600.2405
    • Table 2. Quantitative comparison with state-of-the-art methods currently

      View table

      Table 2. Quantitative comparison with state-of-the-art methods currently

      ModelF-actinERCCPs
      PSNR LPIPS PSNR LPIPS PSNR LPIPS
      ELAN3124.620.398125.100.235127.080.3961
      DLSN3225.020.266126.000.238728.690.3496
      HAT3326.050.261726.080.243528.560.3297
      DeFiAN3427.140.255726.350.878128.760.3158
      DL-SIM3525.890.289223.510.302727.320.3586
      CMGAN1821.800.5753
      DFCAN1927.360.260127.330.240729.440.3337
      MDAFN (σ=027.680.250926.280.234028.930.2975
      MDAFN (σ=0.4/0.627.240.238226.030.185528.600.2405
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    Junchao Fan, MiaoYunyun, XiuLi Bi, Bin Xiao, Xiaoshuai Huang. Flow‐Based Model for Fluorescence Image Super‐Resolution[J]. Chinese Journal of Lasers, 2024, 51(15): 1507103

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    Paper Information

    Category: Biomedical Optical Imaging

    Received: Jan. 12, 2024

    Accepted: Feb. 27, 2024

    Published Online: Jul. 29, 2024

    The Author Email: Xiao Bin (xiaobin@cqupt.edu.cn)

    DOI:10.3788/CJL240491

    CSTR:32183.14.CJL240491

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