• Laser & Optoelectronics Progress
  • Vol. 57, Issue 14, 141024 (2020)
Qingjiang Chen and Mei Qu*
Author Affiliations
  • School of Science, Xi'an University of Architecture and Technology, Xi'an, Shaanxi 710055, China
  • show less
    DOI: 10.3788/LOP57.141024 Cite this Article Set citation alerts
    Qingjiang Chen, Mei Qu. Low-Light Image Enhancement Based on Cascaded Residual Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141024 Copy Citation Text show less
    Network structure of proposed generator
    Fig. 1. Network structure of proposed generator
    Residual block in the generator network
    Fig. 2. Residual block in the generator network
    Discriminator network structure
    Fig. 3. Discriminator network structure
    Different images. (a) River and car of normal-light images; (b) images with illuminance of 0.2; (c) images with illuminance of 0.35; (d) images with illuminance of 0.5; (e) HSV color space images with illuminance of 0.2; (f) H component; (g) S component; (h) V component
    Fig. 4. Different images. (a) River and car of normal-light images; (b) images with illuminance of 0.2; (c) images with illuminance of 0.35; (d) images with illuminance of 0.5; (e) HSV color space images with illuminance of 0.2; (f) H component; (g) S component; (h) V component
    Flow chart of proposed algorithm
    Fig. 5. Flow chart of proposed algorithm
    Enhanced results of low-light image Starfish by different algorithms. (a) Low-light image; (b) normal-light image; (c) Ref. [6] algorithm; (d) Ref. [7] algorithm; (e) Ref. [8] algorithm; (f) Ref. [9] algorithm; (g) Ref. [10] algorithm; (h) Ref. [11] algorithm; (i) Ref. [12] algorithm; (j) proposed algorithm
    Fig. 6. Enhanced results of low-light image Starfish by different algorithms. (a) Low-light image; (b) normal-light image; (c) Ref. [6] algorithm; (d) Ref. [7] algorithm; (e) Ref. [8] algorithm; (f) Ref. [9] algorithm; (g) Ref. [10] algorithm; (h) Ref. [11] algorithm; (i) Ref. [12] algorithm; (j) proposed algorithm
    Enhanced results of low-light image Man by different algorithms. (a) Low-light image; (b) normal-light image; (c) Ref. [6] algorithm; (d) Ref. [7] algorithm; (e) Ref. [8] algorithm; (f) Ref. [9] algorithm; (g) Ref. [10] algorithm; (h) Ref. [11] algorithm; (i) Ref. [12] algorithm; (j) proposed algorithm
    Fig. 7. Enhanced results of low-light image Man by different algorithms. (a) Low-light image; (b) normal-light image; (c) Ref. [6] algorithm; (d) Ref. [7] algorithm; (e) Ref. [8] algorithm; (f) Ref. [9] algorithm; (g) Ref. [10] algorithm; (h) Ref. [11] algorithm; (i) Ref. [12] algorithm; (j) proposed algorithm
    Enhanced results of low-light image Street by different algorithms. (a) Low-light image; (b) normal-light image; (c) Ref. [6] algorithm; (d) Ref. [7] algorithm; (e) Ref. [8] algorithm; (f) Ref. [9] algorithm; (g) Ref. [10] algorithm; (h) Ref. [11] algorithm; (i) Ref. [12] algorithm; (j) proposed algorithm
    Fig. 8. Enhanced results of low-light image Street by different algorithms. (a) Low-light image; (b) normal-light image; (c) Ref. [6] algorithm; (d) Ref. [7] algorithm; (e) Ref. [8] algorithm; (f) Ref. [9] algorithm; (g) Ref. [10] algorithm; (h) Ref. [11] algorithm; (i) Ref. [12] algorithm; (j) proposed algorithm
    Enhancement results of real low-light image Pocky by different algorithms. (a) Real low-light image; (b) Ref. [6] algorithm; (c) Ref. [7] algorithm; (d) Ref. [8] algorithm; (e) Ref. [9] algorithm; (f) Ref. [10] algorithm; (g) Ref. [11] algorithm; (h) Ref. [12] algorithm; (i) proposed algorithm
    Fig. 9. Enhancement results of real low-light image Pocky by different algorithms. (a) Real low-light image; (b) Ref. [6] algorithm; (c) Ref. [7] algorithm; (d) Ref. [8] algorithm; (e) Ref. [9] algorithm; (f) Ref. [10] algorithm; (g) Ref. [11] algorithm; (h) Ref. [12] algorithm; (i) proposed algorithm
    Enhancement results of real low-light image Palace by different algorithms. (a) Real low-light image; (b) Ref. [6] algorithm; (c) Ref. [7] algorithm; (d) Ref. [8] algorithm; (e) Ref. [9] algorithm; (f) Ref. [10] algorithm; (g) Ref. [11] algorithm; (h) Ref. [12] algorithm; (i) proposed algorithm
    Fig. 10. Enhancement results of real low-light image Palace by different algorithms. (a) Real low-light image; (b) Ref. [6] algorithm; (c) Ref. [7] algorithm; (d) Ref. [8] algorithm; (e) Ref. [9] algorithm; (f) Ref. [10] algorithm; (g) Ref. [11] algorithm; (h) Ref. [12] algorithm; (i) proposed algorithm
    Comparison results of different algorithms. (a) Average; (b) average gradient
    Fig. 11. Comparison results of different algorithms. (a) Average; (b) average gradient
    Subjective comparison of low-light image enhancement results by generator network and generative adversarial network. (a) Low-light image; (b) normal-light image; (c) results of generator network; (d) results of generative adversarial network
    Fig. 12. Subjective comparison of low-light image enhancement results by generator network and generative adversarial network. (a) Low-light image; (b) normal-light image; (c) results of generator network; (d) results of generative adversarial network
    Comparison results of generator network and generative adversarial network of different images. (a) PSNR; (b) SSIM
    Fig. 13. Comparison results of generator network and generative adversarial network of different images. (a) PSNR; (b) SSIM
    ImageEvaluationindexMethod
    Ref. [6]Ref. [7]Ref. [8]Ref. [9]Ref. [10]Ref. [11]Ref. [12]Proposed
    StarfishPSNR /dB17.529921.579918.559614.813915.161914.916123.733124.6770
    SSIM0.88440.90870.79740.79630.73540.77670.92330.9301
    BridgePSNR /dB18.769819.694119.438715.112216.049817.379622.727724.1884
    SSIM0.78040.80040.71340.63700.66630.75110.78640.8156
    ManPSNR /dB20.177316.866217.365018.248118.826119.505116.981123.0426
    SSIM0.86550.80690.79020.83390.81390.86890.82600.9097
    BoatPSNR /dB16.696020.189718.117713.380713.968016.687622.343422.6587
    SSIM0.80790.82720.75640.66260.69140.82600.84980.8643
    StreetPSNR /dB17.510819.350218.189215.751015.956420.441718.652525.6980
    SSIM0.86820.87550.78870.80370.75860.89520.88160.9387
    Table 1. PSNR and SSIM of different algorithms
    Qingjiang Chen, Mei Qu. Low-Light Image Enhancement Based on Cascaded Residual Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141024
    Download Citation