Laser & Optoelectronics Progress, Volume. 61, Issue 10, 1037003(2024)

Deep Iterative Filter Adaptive Network for Simple Lens Imaging System

Yi Huang* and Tao Xiong
Author Affiliations
  • Huazhong Institute of Electro-Optics, Wuhan National Laboratory for Optoelectronics, Wuhan 430223, Hubei, China
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    Figures & Tables(13)
    The process of making the optical lens. (a) Self-designed single glued lens; (b) PSF of lens at 20° FOV
    Deep iterative adaptive convolution
    Deblurring network with DIFAN
    Comparison of different restoration methods based on simulation dataset. (a) Original blurred image; (b) recovery results of ISD-Deblur; (c) recovery results of DeblurGAN-v2 ; (d) recovery results of proposed DIFAN; (e) ground truth images
    Comparison of network restoration effects based on simulation datasets at different field angles
    Network restoration effect of simulation data set with a plano-convex lens at 15° field of view angle
    Distortion correction and display-capture experiments
    Image pairs taken using display capture devices
    Experimental results. (a) The original blurred images; (b) image recovery results from the network; (c) ground truth images
    • Table 1. Comparison of ablation studies

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      Table 1. Comparison of ablation studies

      DIFANEvaluations on the DPDD-SL dataset24Computational costs
      PSNR↑SSIM↑MAE↓LPIPS↓Params /106MACs /109
      FP23.880.7230.0410.36810.57364.3
      FP+DIAC25.780.7890.0350.280
      FP+DIAC+BPR26.370.8240.0320.23210.47419.5
      FP+DIAC+BPR+RBN26.940.8470.0290.221
    • Table 2. Quantitative comparisons of reconstruction performance

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      Table 2. Quantitative comparisons of reconstruction performance

      ModelEvaluations on the DPDD-SL datasetComputational costs
      PSNR↑SSIM↑MAE↓LPIPS↓Params /106MACs /109Time /s
      Original22.150.7170.0490.331
      ISD-Deblur2823.780.750.0390.36945
      Deblur-Ganv2726.010.820.0280.31933.15858.52.67
      Proposed model26.940.8470.0290.22110.47419.50.677
    • Table 3. Quantitative comparison of DIFAN restoration comparison experiments under different field of view angles

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      Table 3. Quantitative comparison of DIFAN restoration comparison experiments under different field of view angles

      ExperimentEvaluations on the DPDD-SL data
      PSNR↑SSIM↑MAE↓LPIPS↓
      15° FOV flat-convex lens30.52410.884270.018590.10433
      20° FOV single glued lens26.94250.846790.029040.22172
      36° FOV single glued lens23.61070.766170.044710.32384
    • Table 4. Quantitative comparison of comparative experiments of different network restoration methods under 15° field of view angle of plano-convex lens

      View table

      Table 4. Quantitative comparison of comparative experiments of different network restoration methods under 15° field of view angle of plano-convex lens

      ModelPSNR↑SSIM↑
      Original24.43860.6278
      Multiscale25.00480.6859
      Fov-GAN25.26530.7486
      Deblur-GAN728.02520.7843
      RRG-GAN1630.41020.8650
      Proposed DIAN30.52410.8842
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    Yi Huang, Tao Xiong. Deep Iterative Filter Adaptive Network for Simple Lens Imaging System[J]. Laser & Optoelectronics Progress, 2024, 61(10): 1037003

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    Paper Information

    Category: Digital Image Processing

    Received: Sep. 21, 2023

    Accepted: Nov. 1, 2023

    Published Online: Apr. 29, 2024

    The Author Email: Yi Huang (huang2020bit@163.com)

    DOI:10.3788/LOP232176

    CSTR:32186.14.LOP232176

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