Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141015(2020)
Low-Illumination Remote Sensing Image Enhancement Based on Conditional Generation Adversarial Network
Fig. 1. CGAN model
Fig. 2. Flow chart of proposed algorithm
Fig. 3. Structure of proposed generator
Fig. 4. Structure of proposed discriminator
Fig. 5. Sigmoid function curves
Fig. 6. Examples of synthetic low-illumination remote sensing images
Fig. 7. Comparison of 256 pixel×256 pixel subjective visual images via different algorithms. (a) Original images; (b) HE; (c) MSR; (d) MSRCR; (e) Dong; (f) proposed algorithm
Fig. 8. Results of 380 pixel×380 pixel images processed by proposed algorithm
Fig. 9. Experimental results of different values of α. (a) Original images; (b) α=2.1; (c) α=4; (d) α=6
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Yanfei Peng, Tingting Du, Yi Gao, Lingling Zi, Yu Sang. Low-Illumination Remote Sensing Image Enhancement Based on Conditional Generation Adversarial Network[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141015
Category: Image Processing
Received: Oct. 23, 2019
Accepted: Dec. 11, 2019
Published Online: Jul. 28, 2020
The Author Email: Peng Yanfei (pengyf75@126.com), Du Tingting (446154772@qq.com)