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Images captured in weak illumination conditions could seriously degrade the image quality.
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2017
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2017
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2017
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C. Guo, C. Y. Li, J. Guo, C. C. Loy, J. Hou, S. Kwong, and R. Cong, “Zero-reference deep curve estimation for low-light image enhancement,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR). , 2020, pp. 1780–1789
2020
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K. Xu, X. Yang, B. Yin, and R. W. Lau, “Learning to restore low-light images via decomposition-and-enhancement,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR). , 2020, p. 2281–2290
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2020
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2021
Closest in time.
H. Jiang, Y. Hao, F. Zou, F. Lin, and S. Han, “A visual navigation system for uav under diverse illumination conditions,” Appl. Arti. Intell. , pp. 1–21, 2021
2021
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R. Liu, L. Ma, J. Zhang, X. Fan, and Z. Luo, “Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR). , 2021, pp. 10 561–10 570
2021
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2021
Closest in time.
Y. Wang, Y. Cao, Z. J. Zha, J. Zhang, Z. Xiong, W. Zhang, and F. Wu, “Progressive retinex: Mutually reinforced illumination-noise perception network for low-light image enhancement,” in Proc. 27th. ACM Int. Conf. on Multimedia. , 2019, pp. 2015–2023
2023
Closest in time.