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Deep Illumination-Aware Dehazing With Low-Light and Detail Enhancement

Authors
Kim, G.Kwon, Junseok
Issue Date
2022
Publisher
Institute of Electrical and Electronics Engineers Inc.
Keywords
Dehazing; image enhancement; low-light enhancement.
Citation
IEEE Transactions on Intelligent Transportation Systems, v.23, no.3, pp 2494 - 2508
Pages
15
Journal Title
IEEE Transactions on Intelligent Transportation Systems
Volume
23
Number
3
Start Page
2494
End Page
2508
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/50875
DOI
10.1109/TITS.2021.3117868
ISSN
1524-9050
1558-0016
Abstract
We present a novel dehazing framework for real-world images that contain both hazy and low-light areas. Dehazing and low-light enhancements are unified by using an illumination map that is estimated using a proposed convolutional neural network. The illumination map is then used as a component for three different tasks: atmospheric light estimation, transmission map estimation, and low-light enhancement, thereby enabling the solving of interrelated low-level vision problems simultaneously. To train the neural network to perform both dehazing and low-light enhancement, we synthesize hazy and low-light images from normal images. Experimental results demonstrate that the proposed method quantitatively and qualitatively outperforms state-of-the-art algorithms in real-world image dehazing. IEEE
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소프트웨어대학 (소프트웨어학부)
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