Image Enhancement of Low Light UAV via Global Illumination Self-aware feature Estimation

Jingyu Niu, Dianwei Wang, Pengfei Han, Jie Fang, Xincheng Ren, Yongrui Qin, Zhijie Xu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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 languageEnglish
Title of host publicationProceedings - 2021 3rd International Conference on Natural Language Processing, ICNLP 2021
EditorsMeghan O'Dell
PublisherInstitute of Electrical and Electronics Engineers Inc.
Chapter9537826
Pages225-231
Number of pages7
ISBN (Electronic)9781665414111
ISBN (Print)9781665414128
DOIs
Publication statusPublished - 26 Mar 2021
Event3rd International Conference on Natural Language Processing - Beijing, China
Duration: 26 Mar 202128 Mar 2021
Conference number: 3

Conference

Conference3rd International Conference on Natural Language Processing
Abbreviated titleICNLP 2021
CountryChina
CityBeijing
Period26/03/2128/03/21

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