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Perceptual Enhancem...
Perceptual Enhancement for Autonomous Vehicles : Restoring Visually Degraded Images for Context Prediction via Adversarial Training
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- Ding, Feng (författare)
- School of Management, Nanchang University, Nanchang, Jiangxi 330031, China.
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- Yu, Keping (författare)
- Global Information and Telecommunication Institute, Waseda University, Tokyo 169-8555, Japan (e-mail: keping.yu@aoni.waseda.jp)
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- Gu, Zonghua (författare)
- Umeå universitet,Institutionen för tillämpad fysik och elektronik
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- Li, Xiangjun (författare)
- School of Software, Nanchang University, Nanchang 330047, China.
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- Shi, Yunqing (författare)
- Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07101 USA.
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School of Management, Nanchang University, Nanchang, Jiangxi 330031, China Global Information and Telecommunication Institute, Waseda University, Tokyo 169-8555, Japan (e-mail: keping.yu@aoni.waseda.jp) (creator_code:org_t)
- IEEE, 2022
- 2022
- Engelska.
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Ingår i: IEEE transactions on intelligent transportation systems (Print). - : IEEE. - 1524-9050 .- 1558-0016. ; 23:7, s. 9430-9441
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Realizing autonomous vehicles is one of the ultimate dreams for humans. However, perceptual information collected by sensors in dynamic and complicated environments, in particular, vision information, may exhibit various types of degradation. This may lead to mispredictions of context followed by more severe consequences. Thus, it is necessary to improve degraded images before employing them for context prediction. To this end, we propose a generative adversarial network to restore images from common types of degradation. The proposed model features a novel architecture with an inverse and a reverse module to address additional attributes between image styles. With the supplementary information, the decoding for restoration can be more precise. In addition, we develop a loss function to stabilize the adversarial training with better training efficiency for the proposed model. Compared with several state-of-the-art methods, the proposed method can achieve better restoration performance with high efficiency. It is highly reliable for assisting in context prediction in autonomous vehicles.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- autonomous vehicle
- Autonomous vehicles
- Context prediction
- deep learning
- Degradation
- generative adversarial network.
- Generative adversarial networks
- Generators
- image processing
- Image restoration
- Navigation
- Training
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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