Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141024(2020)
Low-Light Image Enhancement Based on Cascaded Residual Generative Adversarial Network
Fig. 1. Network structure of proposed generator
Fig. 2. Residual block in the generator network
Fig. 3. Discriminator network structure
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. 5. Flow chart of 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
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
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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
Category: Image Processing
Received: Nov. 22, 2019
Accepted: Dec. 24, 2019
Published Online: Jul. 28, 2020
The Author Email: Qu Mei (862907196@qq.com)