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.
Original language | English |
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Title of host publication | Proceedings - 2021 3rd International Conference on Natural Language Processing, ICNLP 2021 |
Editors | Meghan O'Dell |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Chapter | 9537826 |
Pages | 225-231 |
Number of pages | 7 |
ISBN (Electronic) | 9781665414111 |
ISBN (Print) | 9781665414128 |
DOIs | |
Publication status | Published - 26 Mar 2021 |
Event | 3rd International Conference on Natural Language Processing - Beijing, China Duration: 26 Mar 2021 → 28 Mar 2021 Conference number: 3 |
Conference
Conference | 3rd International Conference on Natural Language Processing |
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Abbreviated title | ICNLP 2021 |
Country/Territory | China |
City | Beijing |
Period | 26/03/21 → 28/03/21 |