Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141024(2020)
Low-Light Image Enhancement Based on Cascaded Residual Generative Adversarial Network
Figures & Tables(14)
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
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
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
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
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
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
Fig. 11. Comparison results of different algorithms. (a) Average; (b) average gradient
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
Fig. 13. Comparison results of generator network and generative adversarial network of different images. (a) PSNR; (b) SSIM
Table 1. PSNR and SSIM of different algorithms
View tableTable 1. PSNR and SSIM of different algorithms
Image Evaluation index Method Ref. [6] Ref. [7] Ref. [8] Ref. [9] Ref. [10] Ref. [11] Ref. [12] Proposed Starfish PSNR /dB 17.5299 21.5799 18.5596 14.8139 15.1619 14.9161 23.7331 24.6770 SSIM 0.8844 0.9087 0.7974 0.7963 0.7354 0.7767 0.9233 0.9301 Bridge PSNR /dB 18.7698 19.6941 19.4387 15.1122 16.0498 17.3796 22.7277 24.1884 SSIM 0.7804 0.8004 0.7134 0.6370 0.6663 0.7511 0.7864 0.8156 Man PSNR /dB 20.1773 16.8662 17.3650 18.2481 18.8261 19.5051 16.9811 23.0426 SSIM 0.8655 0.8069 0.7902 0.8339 0.8139 0.8689 0.8260 0.9097 Boat PSNR /dB 16.6960 20.1897 18.1177 13.3807 13.9680 16.6876 22.3434 22.6587 SSIM 0.8079 0.8272 0.7564 0.6626 0.6914 0.8260 0.8498 0.8643 Street PSNR /dB 17.5108 19.3502 18.1892 15.7510 15.9564 20.4417 18.6525 25.6980 SSIM 0.8682 0.8755 0.7887 0.8037 0.7586 0.8952 0.8816 0.9387
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Qingjiang Chen, Mei Qu. Low-Light Image Enhancement Based on Cascaded Residual Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141024
Paper Information
Category: Image Processing
Received: Nov. 22, 2019
Accepted: Dec. 24, 2019
Published Online: Jul. 28, 2020
The Author Email: Mei Qu (862907196@qq.com)
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