@inproceedings{ad610e3723524f27b6afdc308c2fb4a8,
title = "Image Enhancement of Low Light UAV via Global Illumination Self-aware feature Estimation",
abstract = "UAV images acquired under low light conditions are often characterized by low contrast and poor visual effect. To improve image quality, a low light UAV image enhancement method via global illumination self-aware feature estimation was proposed. First, a novel lightweight GhostNet is introduced to extract deeper image features. Secondly, the self-aware module is used to correct the possible missing information between encoder network and decoder network. Finally, gradient loss and structural similarity loss are used to constrain the network to achieve the goal of edge preservation and detail restoration. Through extensive experiments, the method proposed can effectively improve the visualization effect, and get more natural and real results.",
keywords = "feature extraction, GhostNet, image enhancement, self-aware, UAV image",
author = "Jingyu Niu and Dianwei Wang and Pengfei Han and Jie Fang and Xincheng Ren and Yongrui Qin and Zhijie Xu",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd International Conference on Natural Language Processing, ICNLP 2021 ; Conference date: 26-03-2021 Through 28-03-2021",
year = "2021",
month = mar,
doi = "10.1109/ICNLP52887.2021.00044",
language = "English",
series = "Proceedings - 2021 3rd International Conference on Natural Language Processing, ICNLP 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "225--231",
booktitle = "Proceedings - 2021 3rd International Conference on Natural Language Processing, ICNLP 2021",
}