Optics and Precision Engineering, Volume. 32, Issue 10, 1538(2024)

Cross-level feature aggregation image enhancement with dual-path hybrid attention

Heng YUAN1... Xiaoxue WANG1,*, Tinghao YAN1 and Shengchong ZHANG2 |Show fewer author(s)
Author Affiliations
  • 1College of Software, Liaoning Technical University, Huludao2505, China
  • 2Key Laboratory of Optoelectronic Information Control and Security Technology, Tianjin300308, China
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    Figures & Tables(11)
    Network structure diagram of this paper
    Structure diagram of MDAR
    Structure diagram of DHAB
    Structure diagram of CFAM
    Visual comparison of different algorithms on the LOL dataset
    Visual comparison of different algorithms on the MIT-Adobe 5K dataset
    Ablation experiment visualization of different network modules
    • Table 1. Objective evaluation results of different algorithms on the LOL dataset

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      Table 1. Objective evaluation results of different algorithms on the LOL dataset

      方法PSNR/dB↑SSIM↑LPIPS↓NIQE↓
      BPDHE10.9360.4150.4397.985
      CLAHE13.4140.5510.3616.636
      LIME16.7580.5640.3958.058
      BIMEF13.8750.5750.3267.699
      RetinexNet16.7740.4250.4748.879
      KinD17.6470.7710.5145.189
      URetinex Net19.8410.8260.2354.722
      DSLR14.9820.5960.3764.416
      Uformer18.5470.7210.3214.443
      ALL-E18.2160.7630.2194.200
      EnlightenGAN17.4820.6510.3225.807
      ZeroDCE14.8600.5620.3357.767
      ZeroDCE++16.5700.5910.3285.311
      RUAS18.2260.7170.2706.340
      SCI14.7800.5220.3396.617
      Ours22.3470.8500.1784.153
    • Table 2. Objective evaluation results of different algorithms on the MIT-Adobe 5K dataset

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      Table 2. Objective evaluation results of different algorithms on the MIT-Adobe 5K dataset

      方法PSNR/dB↑SSIM↑LPIPS↓NIQE↓
      BPDHE12.8550.5720.4415.280
      CLAHE15.9550.6080.3755.353
      LIME13.3030.7490.3194.172
      BIMEF17.9680.7970.2984.598
      RetinexNet12.5140.6700.3654.841
      KinD16.2030.7840.2544.242
      URetinex Net14.1840.7540.2423.867
      DSLR20.2430.8280.1534.352
      UFormer21.9170.8700.1853.961
      DDNet18.3080.7760.2203.830
      EnlightenGAN17.9050.8360.2383.865
      ZeroDCE15.9310.7660.2193.882
      ZeroDCE++14.6110.4050.2313.850
      RUAS15.9950.7860.1414.048
      SCI17.4770.8400.2164.122
      Ours22.7030.9030.1373.822
    • Table 3. Comparison results of evaluation indicators of different network modules

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      Table 3. Comparison results of evaluation indicators of different network modules

      模块PSNR/dB↑SSIM↑Time/s↓
      无MDAR21.5050.8610.100
      无PMFB21.8620.8780.108
      无DHAB22.5120.8960.109
      无CFAM21.1730.7320.106
      无混合空洞卷积21.9010.8900.110
      Ours22.7030.9030.112
    • Table 4. Average enhancement time of different method

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      Table 4. Average enhancement time of different method

      BPDHECLAHELIMEBIMEFRetinex NetKinDURetinex NetDSLR
      0.10410.11560.49140.12800.12000.05000.4000.125
      Running time↓UFormerALL-EEnlighten GANZero DCEZero DCE++RUASSCIOurs
      0.1211.0550.0570.0030.00190.0060.0010.112
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    Heng YUAN, Xiaoxue WANG, Tinghao YAN, Shengchong ZHANG. Cross-level feature aggregation image enhancement with dual-path hybrid attention[J]. Optics and Precision Engineering, 2024, 32(10): 1538

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    Paper Information

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    Received: Nov. 21, 2023

    Accepted: --

    Published Online: Jul. 8, 2024

    The Author Email: WANG Xiaoxue (wxx2585354184@163.com)

    DOI:10.37188/OPE.20243210.1538

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