Hongqiang Ma, Shiping Ma, Yuelei Xu, Mingming Zhu. Low-Light Image Enhancement Based on Deep Convolutional Neural Network[J]. Acta Optica Sinica, 2019, 39(2): 0210004
- Acta Optica Sinica
- Vol. 39, Issue 2, 0210004 (2019)
Fig. 1. Typical structure of CNN
Fig. 2. Flow chart of proposed algorithm
Fig. 3. Network structure of DCNN model
Fig. 4. Subjective visual comparison of different methods for synthetic low-light images. (a) Image “caps”; (b) image “carnivaldolls”; (c) image “cemetry”; (d) image “building 2”
Fig. 5. Convergence performance of HSI and RGB enhancement methods with BN and without BN. (a) Average SSIM within 50 epochs; (b) average PSNR within 50 epochs
Fig. 6. Subjective visual comparison of different methods for real low-light images. (a) Image from DICM dataset; (b) image from VV dataset; (c)-(d) image from NASA dataset; (e) enlarged result of part shown in blue box of Fig. 6(d)
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Table 1. Tested PSNR under different network layers and convolution kernel numbers
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Table 2. Objective evaluation index of different methods for synthetic low-light images
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Table 3. Objective evaluation index of differentmethods for real low-light images
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